Noisebridge - User contributions [en]

DreamTeam/Brainduinov2

2018-11-01T04:53:09Z

192.195.80.12:

June 2018. Masahiro brings the latest Brainduino EEG from his recent work at Neurofox in Berlin. We'll be meeting Masahiro at Noisebridge on Friday June 8th and at CircuitLaunch on Saturday June 9th and possibly also Sunday June 10th.

[[File:brainduino.png]]

This Brainduino is built from several modules soldered onto a main board using a Teemsy 3.2 to pull 24-bit samples via SPI from an Analog Devices AD7173 ADC. This data is transmitted via serial port to a separate Bluetooth chip which can provide a reliable data stream at rates such as 250 samples per second for 2 channels. The system is can be powered via 5 volt USB external battery pack, or optionally with an integrated internal battery.

Seperate modules provide signal amplification and filtering via op amps (Analog Devices OP4177, Burr-Brown OPA2111) and a low-pass filter (Maxim MAX7480), along with necessary voltage converters etc.

----

[[File:Blueboard.png]]

OP4177 and OPA2111 op amps on the "blue" modules above are arranged to provide 2 pairs of differential amplifiers boosting the approximately 100 microvolt EEG signal by a factor of 1000. Maximum input up to +/- 2.5 millivolts is amplified to produce output of +/- 2.5 volts. D-Amp converts +/- 2.5 volts to 0-5 volt range.

----

[[File:Redboard.png]]

The "red" module has 4 of the MAX7480 8th-order lowpass Butterworth switched-capacitor filters. The cutoff frequency for these filters can be set via software. There are 4 low-pass filters because we have balanced output from the the amplifier board. There is a 555 timer producing a clock signal which can be used to provide a default clock for the MAX7480 lowpass filters cutoff at 32 Hz. With a direct connection bypassing the filter there is a 200 Hz limit due to main amplifier specification - depends on ADC sampling speed. (Errors with too much high frequency going to ADC, resulting in sampling error.)

(The Teensy interrupt timers on this brainduino are usually used instead of the 555).

The module also has the D-Amp for transforming the +/- 2.5 volts to 0-5 volts to connect to ADC.

----

[[File:Greenboard.png]]

The "green" module supports the AD7173 ADC. The ADC is always receiving 0-5 volts on all analog input pins by design. Software allows setup of bipolar mode, meaning that difference between 2 selected pins is calculated. This allows output of potentially 0-10 volts. Pin1 is 0 volts, pin2 is 5 volts, then pin2 - pin1 is 5 volts. In case pin1 is 5 volts and pin2 is 0 volts, then difference is calculated as -5 volts. (...)

Typical range +/- 100 microvolt for EEG signal is amplified by a factor of 1000, resulting in output range of +/- 0.1 volts for the signal of interest (out of the total 10 volt range, which may be driven by larger noise from muscles.) This would seem to indicate roughly 18 bits of our signal reflect the EEG.

http://psychiclab.net/PublicX/

DreamTeam/Reading

2018-10-04T05:02:04Z

192.195.80.12: /* Code */

'''This is essentially the groups meeting notes – a trail of bread crumbs of topics of conversation and projects entertained by the group'''

note: Learn more about previous neuro research at Noisebridge on the wiki... For example, the [[Analog_EEG_Amp]] page describes some project ideas and work done by others here in 2012

==Websites and events that have piqued our interest==
https://noisebridge.net/wiki/NBDSM -- noiseBridge Deepnet and Statistical Mechanics -- first meetup @ noisebridge 7/6/17 at 7PM

https://metacademy.org/
-- machine learning knowledge graph

https://machinelearningguide.libsyn.com/rss -- machine learning guide podcast

http://www.thetalkingmachines.com/ -- podcast

https://karpathy.github.io/2015/05/21/rnn-effectiveness/

http://alexandre.barachant.org/papers/

http://ncs.ethz.ch/publications -- neuromorphic cognitive systems

https://github.com/crillab/gophersat/blob/master/examples/sat-for-noobs.md -- SAT solvers

https://media.ccc.de/v/34c3-8948-low_cost_non-invasive_biomedical_imaging -- Open EIT 34c3 talk https://github.com/OpenEIT

http://acrovirt.org/ -- sensors

http://www.neuroeducate.com/ -- citizen neuroscience

https://www.youtube.com/watch?v=9mZuyUzyN4Q -- "Categories for the Working Hacker"

http://radicalsciencenews.org/599-2/ -- "Deep Learning Fuels Nvidia’s Self-Driving Car Technology"

== Quantum Voodoo (?) ==

https://arxiv.org/pdf/1802.02523.pdf -- "Plasma Brain Dynamics (PBD): a Mechanism for EEG Waves Under Human Consciousness"

== Silent Speech ==

https://dam-prod.media.mit.edu/x/2018/03/23/p43-kapur_BRjFwE6.pdf -- "AlterEgo: A Personalized Wearable Silent Speech Interface"

== Image Reconstruction ==

https://www.biorxiv.org/content/biorxiv/early/2017/12/28/240317.full.pdf -- "Deep image reconstruction from human brain
activity"

== EEG Electrodes ==

https://sites.google.com/site/biofeedbackpages/velcro-sensors -- Saline electrodes

== Generative Adversarial Networks (GAN) ==

https://arxiv.org/pdf/1710.08864 -- "One pixel attack for fooling deep neural networks"

== Kolmolgorov Complexity ==

ftp://ftp.idsia.ch/pub/juergen/loconet.pdf -- "Discovering Neural Nets with Low Kolmolgorov Complexity and High Generalization Capability"

https://papers.nips.cc/paper/394-chaitin-kolmogorov-complexity-and-generalization-in-neural-networks.pdf -- "Chaitin-Kolmogorov Complexity and Generalization in Neural Networks"

== OpenCV ==

http://arnab.org/blog/so-i-suck-24-automating-card-games-using-opencv-and-python

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.585&rep=rep1&type=pdf -- "Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm"

== Category Theory ==

https://arxiv.org/pdf/1711.10455 -- "Backprop as Functor: A compositional perspective on supervised learning"

http://math.ucr.edu/home/baez/rosetta.pdf -- "Physics, Topology, Logic and Computation: A Rosetta Stone"

https://www.youtube.com/watch?v=BF6kHD1DAeU -- "Category theory foundations 1.0 — Steve Awodey"

== Proof Searcher ==

https://arxiv.org/pdf/cs/0207097 -- "Optimal Ordered Problem Solver"

http://people.idsia.ch/~juergen/ultimatecognition.pdf -- "Ultimate Cognition a la Gödel"

http://people.idsia.ch/~juergen/selfreflection.pdf -- "Towards an Actual Gödel Machine Implementation"

== Capsule Models ==

https://arxiv.org/pdf/1710.09829.pdf -- "Dynamic Routing Between Capsules"

https://openreview.net/pdf?id=HJWLfGWRb -- "Matrix Capsules with EM Routing"

== Multivariate Coherence Training ==

https://www.youtube.com/watch?v=qGYjvLki0WY
== Infrared Neuroimaging ==

http://www.ecse.rpi.edu/~yazici/bio_book.pdf -- "Near Infrared Imaging and Spectroscopy for Brain Activity Monitoring"

http://fangyenlab.seas.upenn.edu/pubs/isr.pdf -- "Intrinsic optical signals in neural tissues:
measurements, mechanisms, and applications"

== Geometry ==

http://arxiv.org/abs/1710.10784 -- "How deep learning works --The geometry of deep learning"

== Affective Computing ==

http://affect.media.mit.edu/pdfs/05.ahn-picard-acii.pdf -- "Affective-Cognitive Learning and Decision
Making: A Motivational Reward Framework For Affective Agents"

== Explainability ==

http://arxiv.org/abs/1708.01785 -- "Interpreting CNN knowledge via an Explanatory Graph"

== NLP ==

https://arxiv.org/pdf/1605.06640 -- "Programming with a Differentiable Forth Interpreter"

https://pdfs.semanticscholar.org/f683/dbe8a22d633ad3a2cff379b055b26684a838.pdf -- "Solving General Arithmetic Word Problems"

https://arxiv.org/pdf/1611.04558.pdf -- "Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation"

http://emnlp2014.org/papers/pdf/EMNLP2014162.pdf -- "GloVe: Global Vectors for Word Representation"

== RNNs ==

https://arxiv.org/pdf/1611.01576.pdf -- "Quasi Recurrent Neural Networks"

== Hyper-parameter Optimization ==

https://arxiv.org/abs/1603.06560 -- "Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization"

== Transfer Learning ==

http://arxiv.org/abs/1710.10776v1 -- "Transfer Learning to Learn with Multitask Neural Model Search"

== Reinforcement Learning ==

http://www2.hawaii.edu/~sstill/StillPrecup2011.pdf -- "An information-theoretic approach to curiosity-driven reinforcement learning"

https://arxiv.org/abs/1605.06676 -- "Learning to Communicate with Deep Multi-Agent Reinforcement Learning"

== Learning to Learn ==

https://arxiv.org/pdf/1703.01041.pdf -- "Large-Scale Evolution of Image Classifiers"

https://arxiv.org/pdf/1611.01578 -- "Neural Architecture Search with Reinforcement Learning"

== The Utility of "Noise" in ML ==

https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf -- "Dropout: A Simple Way to Prevent Neural Networks from Overfitting"

http://yann.lecun.com/exdb/publis/pdf/lecun-90b.pdf -- "Optimal Brain Damage"

https://arxiv.org/pdf/1502.01852.pdf -- "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"

== One-shot learning ==

https://arxiv.org/abs/1605%2E06065 -- "One-shot Learning with Memory-Augmented Neural Networks"

== Program Synthesis ==

https://pdfs.semanticscholar.org/0163/35ce7e0a073623e1deac7138b28913dbf594.pdf -- "Human-level concept learning through probabilistic program induction"

https://arxiv.org/pdf/1511.06279.pdf -- "Neural Programmer: Inducing Latent Programs with Gradient Descent"

https://arxiv.org/abs/1608.04428 -- "TerpreT: A Probabilistic Programming Language for Program Induction" Gaunt et al 2016

== Machine Learning Interaction ==

https://teachablemachine.withgoogle.com/#

== Game Theory ==

https://arxiv.org/abs/1707.01068v1 - Maintaining cooperation in complex social dilemmas using deep reinforcement learning

== Questions of Physics and Free Will ==

http://www.scottaaronson.com/papers/giqtm3.pdf - The Ghost in the Quantum Turing Machine

== CNN ==

https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/ - "A Beginner's Guide To Understanding Convolutional Neural Networks"

https://adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/ - "A Beginner's Guide To Understanding Convolutional Neural Networks Part 2"

http://scs.ryerson.ca/~aharley/vis/harley_vis_isvc15.pdf -- "An Interactive Node-Link Visualization
of Convolutional Neural Networks"

http://white.stanford.edu/teach/index.php/An_Introduction_to_Convolutional_Neural_Networks

http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf -- "Learning to Generate Chairs With Convolutional Neural Networks"

http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf
-- "What's Wrong With Deep Learning?"

== Mind-Body Relations ==

http://www.pnas.org/content/111/20/7379.full.pdf -- "Voluntary activation of the sympathetic nervous system and attenuation of the innate immune response in humans"

== Math ==

https://arxiv.org/pdf/1311.1090.pdf -- "Polyhedrons and Perceptrons Are Functionally Equivalent"

Example code and training data using polyhedrons developed by author of above paper: https://www.noisebridge.net/wiki/DreamTeam#Code

== Bayesian Inference ==

https://noisebridge.net/images/e/ef/Perception_is_in_the_Details12.pdf --
"Perception is in the Details: A Predictive Coding Account of the Psychedelic Phenomenon"

http://rsif.royalsocietypublishing.org/content/10/86/20130475 --
"Life as we know it"

http://jmlr.csail.mit.edu/proceedings/papers/v31/wang13b.pdf --
"Collapsed Variational Bayesian Inference for Hidden Markov Models"

http://www.datalab.uci.edu/papers/nips06_cvb.pdf --
"A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation"

http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf --
"Hierarchical Bayesian inference in the visual cortex"

https://www.researchgate.net/profile/Til_Bergmann/publication/262423308_Temporal_coding_organized_by_coupled_alpha_and_gamma_oscillations_prioritize_visual_processing/links/0deec537d1bfda474c000000/Temporal-coding-organized-by-coupled-alpha-and-gamma-oscillations-prioritize-visual-processing.pdf --
"Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing"

http://www.cell.com/neuron/pdf/S0896-6273(15)00823-5.pdf --
"Rhythms for Cognition: Communication through Coherence"

http://www.biorxiv.org/content/biorxiv/early/2014/05/06/004804.full.pdf --
"Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels"

== Speech Recognition ==

https://arxiv.org/pdf/1612.00694v1 -- "ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA"

== Sound Classification ==

https://arxiv.org/pdf/1608.04363v2 -- "Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification"

https://arxiv.org/pdf/1605.09507 "Deep convolutional neural networks for predominant instrument recognition in polyphonic music"

== Hardware Implementations - FPGA, GPU, etc ==

https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf --
"Artificial neural networks in hardware: A survey of two decades of progress"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.133.9185&rep=rep1&type=pdf
"A Self-Repairing Multiplexer-Based FPGA Inspired by Biological Processes"

http://www.genetic-programming.com/jkpdf/fpga1998.pdf -- "Evolving Computer Programs using Rapidly Reconfigurable Field-Programmable Gate Arrays and Genetic Programming"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.2588&rep=rep1&type=pdf -- "Flexible Implementation of Genetic Algorithms on FPGAs"

http://www.users.muohio.edu/jamiespa/html_papers/gem_10.pdf -- "Revisiting Genetic Algorithms for the FPGA Placement Problem"

https://arxiv.org/pdf/1609.09296v1 -- "Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.7533&rep=rep1&type=pdf -- "FPGA-TARGETED NEURAL ARCHITECTURE FOR EMBEDDED ALERTNESS DETECTION"

https://arxiv.org/pdf/1611.02450v1 -- "PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks"

https://arxiv.org/pdf/1605.06402v1 -- "Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks"

https://homes.cs.washington.edu/~luisceze/publications/snnap-hpca-2015.pdf -- "SNNAP: Approximate Computing on Programmable SoCs via Neural Acceleration"

https://arxiv.org/pdf/1701.00485v2 -- "Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices"

== VLSI ==

http://ncs.ethz.ch/pubs/pdf/Indiveri_etal06.pdf -- "A VLSI array of low-power spiking neurons and bistable synapses with spike–timing dependent plasticity"

== Pruning ==

http://papers.nips.cc/paper/5784-learning-both-weights-and-connections-for-efficient-neural-network.pdf --
"Learning both Weights and Connections for Efficient Neural Networks"

https://arxiv.org/pdf/1701.04465 -- "The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning"

https://arxiv.org/pdf/1512.08571 -- "Structured Pruning of Deep Convolutional Neural Networks"

https://arxiv.org/pdf/1611.01427 -- "Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks"

== Efficient Neural Networks via Compression, Quantization, Model Reduction, etc ==

https://arxiv.org/pdf/1504.04788 -- "Compressing Neural Networks with the Hashing Trick"

https://arxiv.org/pdf/1509.08745 -- "Compression of Deep Neural Networks on the Fly"

https://arxiv.org/pdf/1502.03436 -- "An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections"

https://arxiv.org/pdf/1510.00149 -- "Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding"

https://arxiv.org/pdf/1612.00891 -- "Parameter Compression of Recurrent Neural Networks and Degradation of Short-term Memory"

https://arxiv.org/pdf/1609.07061 -- "Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations"

https://arxiv.org/pdf/1607.05418 -- "Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off"

https://arxiv.org/pdf/1602.08194 -- "Scalable and Sustainable Deep Learning via Randomized Hashing"

https://arxiv.org/pdf/1508.05463 -- "StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity"

https://arxiv.org/pdf/1412.7024 -- "Training Deep Neural Networks with Low Precision Multiplications"

https://arxiv.org/pdf/1612.03940 -- "Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks"

https://arxiv.org/pdf/1609.00222 -- "Ternary Neural Networks for Resource-Efficient AI Applications"

== Neural Network Hyperparameter Optimization ==

https://arxiv.org/pdf/1601.00917 -- "DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks"

== Neural Network based EEG Analysis ==
end
http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"

http://inter-eng.upm.ro/2012/files/proceedings/papers/paper72.pdf --
"Neural Network Parallelization on FPGA Platform for EEG Signal Classification"

== Seizure Detection ==

also see https://noisebridge.net/wiki/Kaggle for a (September 2016) current project!

and https://github.com/kevinjos/kaggle-aes-seizure-prediction (some earlier exploration, November 2014)

(broken link, sorry) http://www.sersc.org/journals/ijsip/vol7_no5/26.pdf --
"A Neural Network Model for Predicting Epileptic Seizures based on Fourier-Bessel Functions"

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf --
"A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"

(another broken link) http://cs.uni-muenster.de/Professoren/Lippe/diplomarbeiten/html/eisenbach/Untersuchte%20Artikel/PPHD+00.pdf --
"Recurrent neural network based preenddiction of epileptic seizures in intra- and extracranial EEG"

== Visible Light Sensor Network ==

http://infoteh.etf.unssa.rs.ba/zbornik/2016/radovi/KST-1/KST-1-15.pdf --
"Analysis of Visible Light Communication System for Implementation in Sensor Networks"

== Neurophysiology ==

http://www.buzsakilab.com/content/PDFs/BuzsakiKoch2012.pdf -- "The origin of extracellular fields and
currents — EEG, ECoG, LFP and spikes"

== Signal Processing ==

http://provideyourown.com/2011/analogwrite-convert-pwm-to-voltage/ -- "Arduino’s AnalogWrite – Converting PWM to a Voltage"

http://sim.okawa-denshi.jp/en/PWMtool.php -- "RC Low-pass Filter Design for PWM (Transient Analysis Calculator)"

== Hyperdimensional Computing ==

http://redwood.berkeley.edu/pkanerva/papers/kanerva09-hyperdimensional.pdf --
"Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors"

http://arxiv.org/pdf/1602.03032.pdf --
"Associative Long Short-Term Memory"

== Bird Flocks and Maximum Entropy ==

http://arxiv.org/pdf/1107.0604v1 --
"Statistical Mechanics and Flocks of Birds"

http://arxiv.org/pdf/1307.5563v1 --
"Social interactions dominate speed control in driving natural flocks toward criticality"

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_penfield.pdf --
"Information and Entropy (Course Notes)"

== Whale Songs ==

https://arxiv.org/pdf/1307.0589.pdf -- "The Orchive : Data mining a massive bioacoustic archive"

https://www.researchgate.net/profile/Herbert_Roitblat/publication/13429327_The_neural_network_classification_of_false_killer_whale_%28Pseudorca_crassidens%29_vocalizations/links/540d2ff60cf2df04e75478cd.pdf -- "The neural network classification of false killer whale (Pseudorca crassidens) vocalizations"

http://users.iit.demokritos.gr/~paliourg/papers/PhD.pdf -- "REFINEMENT OF TEMPORAL CONSTRAINTS IN AN EVENT RECOGNITION SYSTEM USING SMALL DATASETS"

https://www.nersc.no/sites/www.nersc.no/files/master_thesis_sebastian_menze.pdf -- "Estimating fin whale distribution from ambient noise spectra using Bayesian inversion"

http://sis.univ-tln.fr/~glotin/IJCNN2015_IHMMbioac_BartChamGlot.pdf -- "Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis"

https://www.inf.ed.ac.uk/publications/thesis/online/IM030057.pdf -- "Hidden Markov Model Clustering of Acoustic Data"

http://danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/ -- Using deep learning to listen for whales

== Computational Cognitive Neuroscience ==

http://www.pnas.org/content/110/41/16390.full -- "Indirection and symbol-like processing in the prefrontal cortex and basal ganglia"

http://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/docs/jaf.pdf -- "Connectionism and Cognitive Architecture: A Critical Analysis"

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2728678/pdf/nihms131814.pdf -- Neves et al 2008 "Cell Shape and Negative Links in Regulatory Motifs Together Control Spatial Information Flow in Signaling Networks"

http://psych.colorado.edu/~oreilly/papers/AisaMingusOReilly08.pdf -- "The Emergent Neural Modeling System"

https://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators

== Text Generation ==

http://www.cs.toronto.edu/~ilya/pubs/2011/LANG-RNN.pdf -- "Generating Text with Recurrent Neural Networks"

== Games ==

http://setgame.com/sites/default/files/teacherscorner/COGNITIVE%20MODELING%20WITH%20SET.pdf -- "How to Construct a Believable Opponent using Cognitive Modeling in the Game of Set"

http://www-personal.umich.edu/~charchan/SET.pdf -- "SETs and Anti-SETs: The Math Behind the Game of SET"

http://personal.plattsburgh.edu/quenelgt/talks/set.pdf -- "Introduction to Set"

http://web.engr.illinois.edu/~pbg/papers/set.pdf -- "On the Complexity of the Game of Set"

http://www.warwick.ac.uk/staff/D.Maclagan/papers/set.pdf -- "The Card Game Set"

http://www.math.ucdavis.edu/~anne/FQ2011/set_game.pdf -- "The Game Set"

https://www.youtube.com/watch?v=k2rgzZ2WXKo -- "Best Practices for Procedural Narrative Generation" Chris Martens

== Large Scale Brain Simulation ==

http://www.nowere.net/b/arch/96550/src/1378907656268.pdf -- "A world survey of artificial brain projects, Part I: Large-scale brain simulations"

== Music ==

http://cmr.soc.plymouth.ac.uk/publications/bci-wkshop.pdf -- "ON GENERATING EEG FOR CONTROLLING MUSICAL SYSTEMS"

== Code ==

https://github.com/nbdt/gotrain (our ANN code)

https://github.com/nbdt/openbci-golang-server (EEG data acquisition and visualization software for OpenBCI)

https://github.com/Micah1/neurotech (brainduino code)

== Hidden Markov Models ==

http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf
-- "A Revealing Introduction to Hidden Markov Models"

http://www.jelmerborst.nl/pubs/Borst2013b.pdf
-- "Discovering Processing Stages by combining EEG with Hidden Markov Models"

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf
-- "A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"

http://www.schalklab.org/sites/default/files/misc/Coupled%20hidden%20markov%20model%20for%20electrocorticographic%20signal%20classification.pdf
-- "Coupled Hidden Markov Model for Electrocorticographic Signal Classification"

== Long Short Term Memory ==

http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
-- "Long Short-Term Memory"

ftp://ftp.idsia.ch/pub/juergen/2002_icannMusic.pdf
-- "Learning The Long-Term Structure of the Blues"

http://www.overcomplete.net/papers/nn2012.pdf
-- "A generalized LSTM-like training algorithm for second-order recurrent neural networks"

http://www.iro.umontreal.ca/~eckdoug/papers/2003_nn.pdf
-- "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets"

http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html
-- "Long Short-Term Memory dramatically improves Google Voice etc"

https://arxiv.org/pdf/1511.05552v4.pdf --
"Recurrent Neural Networks Hardware Implementation on FPGA"

http://vast.cs.ucla.edu/sites/default/files/publications/ASP-DAC2017-1352-11.pdf --
"FPGA-based Accelerator for Long Short-Term Memory Recurrent Neural Networks"

== Question Answering ==

http://www.overcomplete.net/papers/bica2012.pdf
-- "Neural Architectures for Learning to Answer Questions"

https://cs.umd.edu/~miyyer/pubs/2014_qb_rnn.pdf
-- "A Neural Network for Factoid Question Answering over Paragraphs"

http://arxiv.org/pdf/1502.05698.pdf
-- "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"

http://www.peterschueller.com/pub/2014/2014_winograd_schemas_relevance_knowledge_graphs.pdf
-- "Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs"

http://ijcai.org/papers15/Papers/IJCAI15-190.pdf
-- "Towards Addressing the Winograd Schema Challenge — Building and Using a Semantic Parser and a Knowledge Hunting Module"

http://arxiv.org/pdf/1506.05869v2.pdf
-- "A Neural Conversational Model"

http://arxiv.org/pdf/1508.05508v1.pdf
-- "Towards Neural Network-based Reasoning"

http://www.visualqa.org/vqa_iccv2015.pdf
-- "VQA: Visual Question Answering"

== Propagators ==
Cells must support three operations:
*add some content
*collect the content currently accumulated
*register a propagator to be notified when the accumulated content changes
*When new content is added to a cell, the cell must merge the addition with the content already present. When a propagator asks for the content of a cell, the cell must deliver a complete summary of the information that has been added to it.
*The merging of content must be commutative, associative, and idempotent. The behavior of propagators must be monotonic with respect to the lattice induced by the merge operation.
*http://web.mit.edu/~axch/www/art.pdf *http://groups.csail.mit.edu/mac/users/gjs/propagators/
*http://dustycloud.org/blog/sussman-on-ai/

== Boosting ==

http://www.cs.princeton.edu/courses/archive/spr08/cos424/readings/Schapire2003.pdf
-- "The Boosting Approach to Machine Learning An Overview"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.9955&rep=rep1&type=pdf
-- "Ensembling Neural Networks: Many Could Be Better Than All"

http://www.cs.columbia.edu/~rocco/Public/mlj9.pdf
-- "Random Classification Noise Defeats All Convex Potential Boosters"

== Support Vector Machines ==

http://research.microsoft.com/en-us/um/people/cburges/papers/svmtutorial.pdf
-- "A Tutorial on Support Vector Machines for Pattern Recognition"

== Wire Length / Small World Networks ==

http://koulakovlab.cshl.edu/publications/koulakov_chklovskii_2000.pdf
-- "A wire length minimization approach to ocular dominance patterns in mammalian visual cortex"

http://papers.nips.cc/paper/1910-foundations-for-a-circuit-complexity-theory-of-sensory-processing.pdf
-- "Foundations for a Circuit Complexity Theory of Sensory Processing"

https://www.nada.kth.se/~cjo/documents/small_world.pdf
-- "Small-World Connectivity and Attractor Neural Networks"

http://www.doc.ic.ac.uk/~mpsha/ShanahanPhysRevEPreprint.pdf
-- "The Dynamical Complexity of Small-World Networks of Spiking Neurons"

http://www.dam.brown.edu/people/elie/papers/small_world.pdf
-- "Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.4501&rep=rep1&type=pdf
-- "Transition from Random to Small-World Neural Networks by STDP Learning Rule"

http://tamar.tau.ac.il/~eshel/papers/J_Neural_Engineering.pdf
-- "Compact self-wiring in cultured neural networks"

== Backpropagation ==

http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf -- "Neural Networks - A Systematic Introduction"

http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf (works through simple example)

http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/

http://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf

also see http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf (entire book "Neural Networks - a Systemic Introduction" by Raul Rojas)

http://work.caltech.edu/lectures.html Hoeffding's inequality, VC Dimension and Back Propagation ANN

http://www.cs.stir.ac.uk/research/publications/techreps/pdf/TR148.pdf ("Learning XOR: exploring the space of a classic problem")

http://www.sysc.pdx.edu/classes/Werbos-Backpropagation%20through%20time.pdf
-- "Backpropagation Through Time: What it Does and How to Do It"

== Computer Vision ==

http://www.cv-foundation.org/openaccess/CVPR2015.py -- some interesting papers here

http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf -- "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"

== Visual Perception (Biological Systems) ==

http://cbcl.mit.edu/publications/ps/Serre_etal_PBR07.pdf
-- "A quantitative theory of immediate visual recognition"

http://www.dam.brown.edu/ptg/REPORTS/Invariance.pdf
-- "Invariance and Selectivity in the Ventral Visual Pathway"

http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf
-- "Hierarchical Bayesian inference in the visual cortex"

== Neural Synchrony ==

http://arxiv.org/pdf/1312.6115.pdf
-- "Neuronal Synchrony in Complex-Valued Deep Networks"

== Spiking Neural Networks ==

http://ncs.ethz.ch/projects/evospike/publications/ICONIP2011%20Springer%20LNCS%20Nutta.pdf -- "EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network"

http://dai.fmph.uniba.sk/~benus/confers/CNGM_ICANN05.pdf -- "Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques"

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3902 -- "Pattern Recognition in a Bucket"

http://www.igi.tugraz.at/maass/psfiles/221.pdf -- "Noise as a Resource for Computation and Learning in Spiking Neural Networks"

http://web.stanford.edu/group/brainsinsilicon/goals.html -- Neurogrid

http://apt.cs.manchester.ac.uk/projects/SpiNNaker/ -- SpiNNaker

http://personalpages.manchester.ac.uk/staff/javier.navaridas/pubs/2012.jpdc.pdf -- more on SpiNNaker

http://apt.cs.manchester.ac.uk/ftp/pub/apt/papers/JavN_ICS09.pdf -- "Understanding the Interconnection Network of SpiNNaker"

==Hierarchical Temporal Memory==

https://github.com/numenta/nupic/wiki/Hierarchical-Temporal-Memory-Theory

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000532 -- "Towards a Mathematical Theory of Cortical Micro-circuits"

==Distributed Neural Networks==

https://www.paypal-engineering.com/2015/01/12/deep-learning-on-hadoop-2-0-2/ -- paypal engineering blog, paper describes implementation of neural network as described by [http://www.maths.bris.ac.uk/~maxvd/reduce_dim.pdf Hinton 2006] on Hadoop

http://www.sciencemag.org/content/suppl/2006/08/04/313.5786.504.DC1/Hinton.SOM.pdf -- supplementary material for [http://www.maths.bris.ac.uk/~maxvd/reduce_dim.pdf Hinton 2006]

http://www.cs.otago.ac.nz/staffpriv/hzy/papers/pdcs03.pdf -- "Parallelization of a Backpropagation Neural Network on a Cluster Computer"

http://arxiv.org/pdf/1404.5997v2.pdf -- "One weird trick for parallelizing convolutional neural networks"

http://www.ics.uci.edu/~jmoorkan/pub/gpusnn-ijcnn.pdf -- "Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors"

==Mixture of Experts==

http://www.cs.toronto.edu/~fritz/absps/jjnh91.pdf -- "Adaptive Mixtures of Local Experts"

==Hopfield nets and RBMs==

http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf -- Yoshua Bengio, 2009, Learning Deep Architectures for AI

http://deeplearning.cs.cmu.edu/ -- Syllabus for cs course on deep learning, possible source of literature for the library

https://class.coursera.org/neuralnets-2012-001/lecture -- Lecture 11 - 15 to aid in understanding of Hinton 2006 paper above

http://www.pnas.org/content/79/8/2554.full.pdf -- "Neural networks and physical systems with emergent collective computational abilities" (Hopfield 1982)

http://page.mi.fu-berlin.de/rojas/neural/chapter/K13.pdf -- "The Hopfield Model" (Rojas 1996)

http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"

http://www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/cameraready_xwjiabibe.pdf -- "A Novel Semi-supervised Deep Learning Framework
for Affective State Recognition on EEG Signals"

http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf -- "A Practical Guide to Training Restricted Boltzmann Machines"

http://arxiv.org/pdf/1503.07793v2.pdf
-- "Gibbs Sampling with Low-Power Spiking Digital Neurons"

http://arxiv.org/pdf/1311.0190v1 -- "On the typical properties of inverse problems in statistical mechanics" Iacopo Mastromatteo 2013

http://jmlr.org/proceedings/papers/v5/salakhutdinov09a/salakhutdinov09a.pdf -- "Deep Boltzmann Machines" Salakhutdinov & Hinton 2009

http://www.cs.toronto.edu/~hinton/absps/tr00-004.pdf -- "Training Products of Experts by Minimizing Contrastive Divergence"

http://www.eecg.toronto.edu/~pc/research/publications/ly.fpga2009.submitted.pdf -- "A High-Performance FPGA Architecture for Restricted
Boltzmann Machines" Ly & Chow 2009

https://pdfs.semanticscholar.org/85fa/f7c3c05388e2bcd097a416606bdd88fc0c7c.pdf -- "A MULTI-FPGA ARCHITECTURE FOR STOCHASTIC RESTRICTED BOLTZMANN MACHINES" Ly & Chow 2009

== Variational Renormalization ==

https://arxiv.org/pdf/1410.3831 -- "An exact mapping between the Variational Renormalization Group and Deep Learning"

== Neuromorphic Stuff ==

https://arxiv.org/pdf/1508.01008.pdf -- "INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks" Chung, Shin & Kang 2015

== Markov Chain Monte Carlo ==

http://www.cs.princeton.edu/courses/archive/spr06/cos598C/papers/AndrieuFreitasDoucetJordan2003.pdf
-- "An Introduction to MCMC for Machine Learning"

http://jmlr.org/proceedings/papers/v37/salimans15.pdf
-- "Markov Chain Monte Carlo and Variational Inference: Bridging the Gap"

https://www.umiacs.umd.edu/~resnik/pubs/LAMP-TR-153.pdf -- "Gibbs Sampling for the Uninitiated"

==Entrainment==

http://www.scirp.org/Journal/PaperDownload.aspx?paperID=33251 -- "Alpha Rhythms Response to 10 Hz Flicker Is Wavelength Dependent"

http://www.brainmachine.co.uk/wp-content/uploads/Herrmann_Flicker.pdf -- "EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena"

http://www.jneurosci.org/content/23/37/11621.full.pdf -- "Human Cerebral Activation during Steady-State Visual-Evoked Responses"

http://www.dauwels.com/Papers/CogDyn%202009.pdf -- "On the synchrony of steady state visual evoked potentials and oscillatory burst events"

https://www.tu-ilmenau.de/fileadmin/public/lorentz-force/publications/peer/2012/haueisen2012/Halbleib_JCN_2012_Topographic_analysis_photic_driving.pdf -- "Topographic Analysis of Engagement and Disengagement of Neural Oscillators in Photic Driving: A Combined Electroencephalogram/Magnetoencephalogram Study"

==Mining Scientific Literature==

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC153503/pdf/1471-2105-4-11.pdf -- "PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine" Donaldson 2003 BMC Bioinformatics

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674139/pdf/pcbi.1004630.pdf -- "Text Mining for Protein Docking" Badal 2015 PLoS Comput Biol.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4691339/pdf/bav116.pdf -- "Biocuration with insufficient resources and fixed timelines" Rodriguez-Esteban 2015 Database: The Journal of Biological Databases and Curation

==(not necessarilly very) Current Discussion==

re Tononi's "Integrated Information Theory" http://www.scottaaronson.com/blog/?p=1799

(19 February 2014) starting to think about possibility for experiments (loosely) related to [https://en.wikipedia.org/wiki/Visual_evoked_potential Visual Evoked Potential] research again - for instance:

http://www.biosemi.com/publications/pdf/Bierman.pdf -- Wave function collapse

[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999

[[File:CoherentEEGAmbiguousFigureBinding.pdf]] -- "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" -- Klemm, Li, and Hernandez 2000

Note these two papers flog coherence measures - not trying to focus so much on that analysis right now, more interested in general understanding of what these experiments are about with possible goal of designing simpler experiments & analysis of similar perceptual/cognitive phenomena.

Here is an article that looks more directly at visual evoked potential measures:

[[File:ERP_Stereoscopic.pdf]] -- "Evoked Responses to Distinct and Nebulous Stereoscopic Stimuli" -- Dunlop et al 1983

(11 September 2013) more on analysis methods:

http://slesinsky.org/brian/misc/eulers_identity.html

http://www.dspguide.com/ch8/1.htm

[[File:Fftw3.pdf]]

[[File:ParametricEEGAnalysis.pdf]]

[[File:ICATutorial.pdf]]

[[File:ICAFrequencyDomainEEG.pdf]]

(21 August 2013) - readings relating statistical (etc math / signal processing / pattern recognition / machine learning) methods for EEG data interpretation. A lot of stuff, a bit of nonsense ... and ... statistics!

Would be good to identify any papers suitable for more in-depth study. Currently have a wide field to graze for selections:

[[File:DWTandFFTforEEG.pdf]] "EEG Classifier using Fourier Transform and Wavelet Transform" -- Maan Shaker, 2007

[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999

[[File:KulaichevCoherence.pdf]] -- "The Informativeness of Coherence Analysis in EEG Studies" -- A. P. Kulaichev 2009 ''note: interesting critical perspective re limitations, discussion of alternative analytics''

[[File:ContinuousAndDiscreteWaveletTransforms.pdf]] -- review of (pre-1990) wavelet literature -- Christopher Heil and David Walnut, 1989

[[File:EEGGammaMeditation.pdf]] -- "Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self" -- Dietrich Lehman et al 2001

http://neuro.hut.fi/~pavan/home/Hyvarinen2010_FourierICA_Neuroimage.pdf - "Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis" -- Aapo Hyvarinen, Pavan Ramkumar, Lauri Parkkonen, Riitta Hari - paper published in Neuroimage vol 49 (2010)

----

[http://www.nickgillian.com/software/grt OpenSource Machine Learning Algs from NG @MIT]
 [https://www.usenix.org/system/files/conference/usenixsecurity12/sec12-final56.pdf Consumer grade EEG used to see "P300" reponse] and for thoes with a short attention span [http://www.extremetech.com/extreme/134682-hackers-backdoor-the-human-brain-successfully-extract-sensitive-data tldr]
 (discussed at meetup Wednesday 31 July 2013)
 "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" Klemm, Li, and Hernandez 2000
 [[File:CoherentEEGAmbiguousFigureBinding.pdf]]
 "We tested the hypothesis that perception of an alternative image in ambiguous figures would be manifest as high-frequency (gamma) components that become synchronized over multiple scalp sites as a "cognitive binding" process occurs."
 

----

[http://dreamsessions.net art, dream, and eeg]

----

[http://www.believermag.com/issues/200710/?read=article_aviv mind v brain, hobson v solms]
 http://en.wikipedia.org/wiki/Activation-synthesis_hypothesis
 [[File:HobsomREMDreamProtoconsciousness.pdf|Hobson09ProtosconsciousnessREMDream]]

"Hobson and McCarley originally proposed in the 1970s that the differences in the waking-NREM-REM sleep cycle was the result of interactions between aminergic REM-off cells and cholinergic REM-on cells.[4] This was perceived as the activation-synthesis model, stating that brain activation during REM sleep results in synthesis of dream creation.[1][1] Hobson's five cardinal characteristics include: intense emotions, illogical content, apparent sensory impressions, uncritical acceptance of dream events, and difficulty in being remembered."

----

Berkeley Labs

[http://gallantlab.org/index.html Gallant Group]
 [http://walkerlab.berkeley.edu/ Walker Group]
 [http://socrates.berkeley.edu/~plab/ Palmer Group]

==Sleep Research==

[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2335403/ Comment on the AASM Manual for the Scoring of Sleep and Associated Events]

==random tangents==
(following previous discussion) - we might select a few to study in more depth
(... or not! Plenty more to explore - suggestions (random or otherwise) are welcome.
http://www.meltingasphalt.com/neurons-gone-wild/ --
Neurons Gone Wild - Levels of agency in the brain.

'''stereoscopic perception:'''
*[[File:ERP_Stereoscopic.pdf]]

some (maybe) interesting background on Information Theory (cool title...)
Claude Shannon: "Communication in the Presence of Noise"
[[File:Shannon_noise.pdf]]
"We will call a system that transmits without errors at the rate ''C'' an ideal system.
Such a system cannot be achieved with any finite encoding process
but can be approximated as closely as desired."

wikipedia etc quick reads:
https://en.wikipedia.org/wiki/Eeg
https://en.wikipedia.org/wiki/Neural_synchronization
https://en.wikipedia.org/wiki/Event-related_potentials
http://www.scholarpedia.org/article/Spike-and-wave_oscillations
http://www.scholarpedia.org/article/Thalamocortical_oscillations

==Previously==

[http://www.psychiclab.net/ Masahiro's EEG Device/IBVA Software]

[http://www.instructables.com/id/open-brain-wave-interface-hardware-1/ and ... open source hardware design and kits on instructables.com]

[http://brainstorms.puzzlebox.info/ Puzzlebox - Opensource BCI Developers]

Morgan from GazzLab @ MissionBay/UCSF

https://github.com/gazzlab

Let's ease into a lightweight "journal club" discussion with this technical report from NeuroSky.

Name: A user-friendly SSVEP-based brain-computer interface using a time-domain classifier, Luo A and Sullivan TJ 2010

URL: [[File:NeuroSkyVEP.pdf‎]]

Please add your comments & questions here.

==Background Reading==

http://nanosouffle.net/ (view into Arxiv.org)

Name: Hunting for Meaning after Midnight, Miller 2007

URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0002.pdf>

Name: Broken mirrors, Ram, VS, & Oberman, LM, 2006, Nov

URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0003.pdf>

Ramachandran Critique

http://blogs.scientificamerican.com/guest-blog/2012/11/06/whats-so-special-about-mirror-neurons/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773693/

Sleep/Dream Studies

http://www.cns.atr.jp/dni/en/publications/

==NeuroSky Docs==
[[File:NeuroSkyDongleProtocol.pdf‎]]

[[File:NeuroSkyCommunicationsProtocol.pdf‎]]

==Android Neutral Network Fuzzy Learning app==
[https://play.google.com/store/apps/details?id=com.faadooengineers.free_neuralnetworkandfuzzysystems Android Neutral Network Fuzzy Learning app in Play Store]

==Learning about Neural Networks==
* What type of network? [http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma RMB (Restricted Boltzmann Machine) vs Autoencoder/MLP vs CNN (Convolutional Neural Networks)]
* Andrej Karpathy's [http://cs.stanford.edu/people/karpathy/convnetjs/ Convolutional Neural Network coded in JavaScript (ConvNetJS)]
* Andrej Karpathy's [http://karpathy.github.io/2015/10/25/selfie/ What a Deep Neural Network thinks about your #selfie (background on Convolutional Neural Networks for image recognition and classification)]
* [https://blog.webkid.io/neural-networks-in-javascript/ Neural Networks in JavaScript w/MNIST]
* [http://www.antoniodeluca.info/blog/10-08-2016/neural-networks-in-javascript.html Another NN in JS]
* [http://caza.la/synaptic/ The Synaptic "architecture-free" neural network library in JS]

DreamTeam/Reading

2018-04-12T03:52:09Z

192.195.80.12:

(note wiki contains some useful clues re previous neuro research at Noisebridge ... For example, the [[Analog_EEG_Amp]] page describes some project ideas and work done by others here in 2012)

https://noisebridge.net/wiki/NBDSM -- noiseBridge Deepnet and Statistical Mechanics -- first meetup @ noisebridge 7/6/17 at 7PM

https://metacademy.org/
-- machine learning knowledge graph

https://machinelearningguide.libsyn.com/rss -- machine learning guide podcast

http://www.thetalkingmachines.com/ -- podcast

https://karpathy.github.io/2015/05/21/rnn-effectiveness/

http://alexandre.barachant.org/papers/

http://ncs.ethz.ch/publications -- neuromorphic cognitive systems

https://github.com/crillab/gophersat/blob/master/examples/sat-for-noobs.md -- SAT solvers

https://media.ccc.de/v/34c3-8948-low_cost_non-invasive_biomedical_imaging -- Open EIT 34c3 talk https://github.com/OpenEIT

http://acrovirt.org/ -- sensors

http://www.neuroeducate.com/ -- citizen neuroscience

https://www.youtube.com/watch?v=9mZuyUzyN4Q -- "Categories for the Working Hacker"

http://radicalsciencenews.org/599-2/ -- "Deep Learning Fuels Nvidia’s Self-Driving Car Technology"

== EEG Electrodes ==

https://sites.google.com/site/biofeedbackpages/velcro-sensors -- Saline electrodes

== Generative Adversarial Networks (GAN) ==

https://arxiv.org/pdf/1710.08864 -- "One pixel attack for fooling deep neural networks"

== OpenCV ==

http://arnab.org/blog/so-i-suck-24-automating-card-games-using-opencv-and-python

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.585&rep=rep1&type=pdf -- "Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm"

== Category Theory ==

https://arxiv.org/pdf/1711.10455 -- "Backprop as Functor: A compositional perspective on supervised learning"

http://math.ucr.edu/home/baez/rosetta.pdf -- "Physics, Topology, Logic and Computation: A Rosetta Stone"

https://www.youtube.com/watch?v=BF6kHD1DAeU -- "Category theory foundations 1.0 — Steve Awodey"

== Proof Searcher ==

https://arxiv.org/pdf/cs/0207097 -- "Optimal Ordered Problem Solver"

http://people.idsia.ch/~juergen/ultimatecognition.pdf -- "Ultimate Cognition a la Gödel"

http://people.idsia.ch/~juergen/selfreflection.pdf -- "Towards an Actual Gödel Machine Implementation"

== Capsule Models ==

https://arxiv.org/pdf/1710.09829.pdf -- "Dynamic Routing Between Capsules"

https://openreview.net/pdf?id=HJWLfGWRb -- "Matrix Capsules with EM Routing"

== Infrared Neuroimaging ==

http://www.ecse.rpi.edu/~yazici/bio_book.pdf -- "Near Infrared Imaging and Spectroscopy for Brain Activity Monitoring"

== Geometry ==

http://arxiv.org/abs/1710.10784 -- "How deep learning works --The geometry of deep learning"

== Affective Computing ==

http://affect.media.mit.edu/pdfs/05.ahn-picard-acii.pdf -- "Affective-Cognitive Learning and Decision
Making: A Motivational Reward Framework For Affective Agents"

== Explainability ==

http://arxiv.org/abs/1708.01785 -- "Interpreting CNN knowledge via an Explanatory Graph"

== NLP ==

https://arxiv.org/pdf/1605.06640 -- "Programming with a Differentiable Forth Interpreter"

https://pdfs.semanticscholar.org/f683/dbe8a22d633ad3a2cff379b055b26684a838.pdf -- "Solving General Arithmetic Word Problems"

https://arxiv.org/pdf/1611.04558.pdf -- "Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation"

http://emnlp2014.org/papers/pdf/EMNLP2014162.pdf -- "GloVe: Global Vectors for Word Representation"

== RNNs ==

https://arxiv.org/pdf/1611.01576.pdf -- "Quasi Recurrent Neural Networks"

== Hyper-parameter Optimization ==

https://arxiv.org/abs/1603.06560 -- "Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization"

== Transfer Learning ==

http://arxiv.org/abs/1710.10776v1 -- "Transfer Learning to Learn with Multitask Neural Model Search"

== Reinforcement Learning ==

http://www2.hawaii.edu/~sstill/StillPrecup2011.pdf -- "An information-theoretic approach to curiosity-driven reinforcement learning"

https://arxiv.org/abs/1605.06676 -- "Learning to Communicate with Deep Multi-Agent Reinforcement Learning"

== Learning to Learn ==

https://arxiv.org/pdf/1703.01041.pdf -- "Large-Scale Evolution of Image Classifiers"

https://arxiv.org/pdf/1611.01578 -- "Neural Architecture Search with Reinforcement Learning"

== The Utility of "Noise" in ML ==

https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf -- "Dropout: A Simple Way to Prevent Neural Networks from Overfitting"

http://yann.lecun.com/exdb/publis/pdf/lecun-90b.pdf -- "Optimal Brain Damage"

https://arxiv.org/pdf/1502.01852.pdf -- "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"

== One-shot learning ==

https://arxiv.org/abs/1605%2E06065 -- "One-shot Learning with Memory-Augmented Neural Networks"

== Program Synthesis ==

https://pdfs.semanticscholar.org/0163/35ce7e0a073623e1deac7138b28913dbf594.pdf -- "Human-level concept learning through probabilistic program induction"

https://arxiv.org/pdf/1511.06279.pdf -- "Neural Programmer: Inducing Latent Programs with Gradient Descent"

https://arxiv.org/abs/1608.04428 -- "TerpreT: A Probabilistic Programming Language for Program Induction" Gaunt et al 2016

== Machine Learning Interaction ==

https://teachablemachine.withgoogle.com/#

== Game Theory ==

https://arxiv.org/abs/1707.01068v1 - Maintaining cooperation in complex social dilemmas using deep reinforcement learning

== Questions of Physics and Free Will ==

http://www.scottaaronson.com/papers/giqtm3.pdf - The Ghost in the Quantum Turing Machine

== CNN ==

https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/ - "A Beginner's Guide To Understanding Convolutional Neural Networks"

https://adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/ - "A Beginner's Guide To Understanding Convolutional Neural Networks Part 2"

http://scs.ryerson.ca/~aharley/vis/harley_vis_isvc15.pdf -- "An Interactive Node-Link Visualization
of Convolutional Neural Networks"

http://white.stanford.edu/teach/index.php/An_Introduction_to_Convolutional_Neural_Networks

http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf -- "Learning to Generate Chairs With Convolutional Neural Networks"

http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf
-- "What's Wrong With Deep Learning?"

== Mind-Body Relations ==

http://www.pnas.org/content/111/20/7379.full.pdf -- "Voluntary activation of the sympathetic nervous system and attenuation of the innate immune response in humans"

== Math ==

https://arxiv.org/pdf/1311.1090.pdf -- "Polyhedrons and Perceptrons Are Functionally Equivalent"

Example code and training data using polyhedrons developed by author of above paper: https://www.noisebridge.net/wiki/DreamTeam#Code

== Bayesian Inference ==

https://noisebridge.net/images/e/ef/Perception_is_in_the_Details12.pdf --
"Perception is in the Details: A Predictive Coding Account of the Psychedelic Phenomenon"

http://rsif.royalsocietypublishing.org/content/10/86/20130475 --
"Life as we know it"

http://jmlr.csail.mit.edu/proceedings/papers/v31/wang13b.pdf --
"Collapsed Variational Bayesian Inference for Hidden Markov Models"

http://www.datalab.uci.edu/papers/nips06_cvb.pdf --
"A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation"

http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf --
"Hierarchical Bayesian inference in the visual cortex"

https://www.researchgate.net/profile/Til_Bergmann/publication/262423308_Temporal_coding_organized_by_coupled_alpha_and_gamma_oscillations_prioritize_visual_processing/links/0deec537d1bfda474c000000/Temporal-coding-organized-by-coupled-alpha-and-gamma-oscillations-prioritize-visual-processing.pdf --
"Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing"

http://www.cell.com/neuron/pdf/S0896-6273(15)00823-5.pdf --
"Rhythms for Cognition: Communication through Coherence"

http://www.biorxiv.org/content/biorxiv/early/2014/05/06/004804.full.pdf --
"Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels"

== Speech Recognition ==

https://arxiv.org/pdf/1612.00694v1 -- "ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA"

== Sound Classification ==

https://arxiv.org/pdf/1608.04363v2 -- "Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification"

https://arxiv.org/pdf/1605.09507 "Deep convolutional neural networks for predominant instrument recognition in polyphonic music"

== Hardware Implementations - FPGA, GPU, etc ==

https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf --
"Artificial neural networks in hardware: A survey of two decades of progress"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.133.9185&rep=rep1&type=pdf
"A Self-Repairing Multiplexer-Based FPGA Inspired by Biological Processes"

http://www.genetic-programming.com/jkpdf/fpga1998.pdf -- "Evolving Computer Programs using Rapidly Reconfigurable Field-Programmable Gate Arrays and Genetic Programming"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.2588&rep=rep1&type=pdf -- "Flexible Implementation of Genetic Algorithms on FPGAs"

http://www.users.muohio.edu/jamiespa/html_papers/gem_10.pdf -- "Revisiting Genetic Algorithms for the FPGA Placement Problem"

https://arxiv.org/pdf/1609.09296v1 -- "Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.7533&rep=rep1&type=pdf -- "FPGA-TARGETED NEURAL ARCHITECTURE FOR EMBEDDED ALERTNESS DETECTION"

https://arxiv.org/pdf/1611.02450v1 -- "PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks"

https://arxiv.org/pdf/1605.06402v1 -- "Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks"

https://homes.cs.washington.edu/~luisceze/publications/snnap-hpca-2015.pdf -- "SNNAP: Approximate Computing on Programmable SoCs via Neural Acceleration"

https://arxiv.org/pdf/1701.00485v2 -- "Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices"

== VLSI ==

http://ncs.ethz.ch/pubs/pdf/Indiveri_etal06.pdf -- "A VLSI array of low-power spiking neurons and bistable synapses with spike–timing dependent plasticity"

== Pruning ==

http://papers.nips.cc/paper/5784-learning-both-weights-and-connections-for-efficient-neural-network.pdf --
"Learning both Weights and Connections for Efficient Neural Networks"

https://arxiv.org/pdf/1701.04465 -- "The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning"

https://arxiv.org/pdf/1512.08571 -- "Structured Pruning of Deep Convolutional Neural Networks"

https://arxiv.org/pdf/1611.01427 -- "Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks"

== Efficient Neural Networks via Compression, Quantization, Model Reduction, etc ==

https://arxiv.org/pdf/1504.04788 -- "Compressing Neural Networks with the Hashing Trick"

https://arxiv.org/pdf/1509.08745 -- "Compression of Deep Neural Networks on the Fly"

https://arxiv.org/pdf/1502.03436 -- "An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections"

https://arxiv.org/pdf/1510.00149 -- "Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding"

https://arxiv.org/pdf/1612.00891 -- "Parameter Compression of Recurrent Neural Networks and Degradation of Short-term Memory"

https://arxiv.org/pdf/1609.07061 -- "Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations"

https://arxiv.org/pdf/1607.05418 -- "Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off"

https://arxiv.org/pdf/1602.08194 -- "Scalable and Sustainable Deep Learning via Randomized Hashing"

https://arxiv.org/pdf/1508.05463 -- "StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity"

https://arxiv.org/pdf/1412.7024 -- "Training Deep Neural Networks with Low Precision Multiplications"

https://arxiv.org/pdf/1612.03940 -- "Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks"

https://arxiv.org/pdf/1609.00222 -- "Ternary Neural Networks for Resource-Efficient AI Applications"

== Neural Network Hyperparameter Optimization ==

https://arxiv.org/pdf/1601.00917 -- "DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks"

== Neural Network based EEG Analysis ==
end
http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"

http://inter-eng.upm.ro/2012/files/proceedings/papers/paper72.pdf --
"Neural Network Parallelization on FPGA Platform for EEG Signal Classification"

== Seizure Detection ==

also see https://noisebridge.net/wiki/Kaggle for a (September 2016) current project!

and https://github.com/kevinjos/kaggle-aes-seizure-prediction (some earlier exploration, November 2014)

(broken link, sorry) http://www.sersc.org/journals/ijsip/vol7_no5/26.pdf --
"A Neural Network Model for Predicting Epileptic Seizures based on Fourier-Bessel Functions"

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf --
"A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"

(another broken link) http://cs.uni-muenster.de/Professoren/Lippe/diplomarbeiten/html/eisenbach/Untersuchte%20Artikel/PPHD+00.pdf --
"Recurrent neural network based preenddiction of epileptic seizures in intra- and extracranial EEG"

== Visible Light Sensor Network ==

http://infoteh.etf.unssa.rs.ba/zbornik/2016/radovi/KST-1/KST-1-15.pdf --
"Analysis of Visible Light Communication System for Implementation in Sensor Networks"

== Neurophysiology ==

http://www.buzsakilab.com/content/PDFs/BuzsakiKoch2012.pdf -- "The origin of extracellular fields and
currents — EEG, ECoG, LFP and spikes"

== Signal Processing ==

http://provideyourown.com/2011/analogwrite-convert-pwm-to-voltage/ -- "Arduino’s AnalogWrite – Converting PWM to a Voltage"

http://sim.okawa-denshi.jp/en/PWMtool.php -- "RC Low-pass Filter Design for PWM (Transient Analysis Calculator)"

== Hyperdimensional Computing ==

http://redwood.berkeley.edu/pkanerva/papers/kanerva09-hyperdimensional.pdf --
"Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors"

http://arxiv.org/pdf/1602.03032.pdf --
"Associative Long Short-Term Memory"

== Bird Flocks and Maximum Entropy ==

http://arxiv.org/pdf/1107.0604v1 --
"Statistical Mechanics and Flocks of Birds"

http://arxiv.org/pdf/1307.5563v1 --
"Social interactions dominate speed control in driving natural flocks toward criticality"

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_penfield.pdf --
"Information and Entropy (Course Notes)"

== Whale Songs ==

https://arxiv.org/pdf/1307.0589.pdf -- "The Orchive : Data mining a massive bioacoustic archive"

https://www.researchgate.net/profile/Herbert_Roitblat/publication/13429327_The_neural_network_classification_of_false_killer_whale_%28Pseudorca_crassidens%29_vocalizations/links/540d2ff60cf2df04e75478cd.pdf -- "The neural network classification of false killer whale (Pseudorca crassidens) vocalizations"

http://users.iit.demokritos.gr/~paliourg/papers/PhD.pdf -- "REFINEMENT OF TEMPORAL CONSTRAINTS IN AN EVENT RECOGNITION SYSTEM USING SMALL DATASETS"

https://www.nersc.no/sites/www.nersc.no/files/master_thesis_sebastian_menze.pdf -- "Estimating fin whale distribution from ambient noise spectra using Bayesian inversion"

http://sis.univ-tln.fr/~glotin/IJCNN2015_IHMMbioac_BartChamGlot.pdf -- "Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis"

https://www.inf.ed.ac.uk/publications/thesis/online/IM030057.pdf -- "Hidden Markov Model Clustering of Acoustic Data"

http://danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/ -- Using deep learning to listen for whales

== Computational Cognitive Neuroscience ==

http://www.pnas.org/content/110/41/16390.full -- "Indirection and symbol-like processing in the prefrontal cortex and basal ganglia"

http://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/docs/jaf.pdf -- "Connectionism and Cognitive Architecture: A Critical Analysis"

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2728678/pdf/nihms131814.pdf -- Neves et al 2008 "Cell Shape and Negative Links in Regulatory Motifs Together Control Spatial Information Flow in Signaling Networks"

http://psych.colorado.edu/~oreilly/papers/AisaMingusOReilly08.pdf -- "The Emergent Neural Modeling System"

https://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators

== Text Generation ==

http://www.cs.toronto.edu/~ilya/pubs/2011/LANG-RNN.pdf -- "Generating Text with Recurrent Neural Networks"

== Games ==

http://setgame.com/sites/default/files/teacherscorner/COGNITIVE%20MODELING%20WITH%20SET.pdf -- "How to Construct a Believable Opponent using Cognitive Modeling in the Game of Set"

http://www-personal.umich.edu/~charchan/SET.pdf -- "SETs and Anti-SETs: The Math Behind the Game of SET"

http://personal.plattsburgh.edu/quenelgt/talks/set.pdf -- "Introduction to Set"

http://web.engr.illinois.edu/~pbg/papers/set.pdf -- "On the Complexity of the Game of Set"

http://www.warwick.ac.uk/staff/D.Maclagan/papers/set.pdf -- "The Card Game Set"

http://www.math.ucdavis.edu/~anne/FQ2011/set_game.pdf -- "The Game Set"

https://www.youtube.com/watch?v=k2rgzZ2WXKo -- "Best Practices for Procedural Narrative Generation" Chris Martens

== Large Scale Brain Simulation ==

http://www.nowere.net/b/arch/96550/src/1378907656268.pdf -- "A world survey of artificial brain projects, Part I: Large-scale brain simulations"

== Music ==

http://cmr.soc.plymouth.ac.uk/publications/bci-wkshop.pdf -- "ON GENERATING EEG FOR CONTROLLING MUSICAL SYSTEMS"

== Code ==

https://github.com/nbdt/gotrain (our ANN code)

https://github.com/nbdt/openbci-golang-server (EEG data acquisition and visualization software for OpenBCI)

== Hidden Markov Models ==

http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf
-- "A Revealing Introduction to Hidden Markov Models"

http://www.jelmerborst.nl/pubs/Borst2013b.pdf
-- "Discovering Processing Stages by combining EEG with Hidden Markov Models"

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf
-- "A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"

http://www.schalklab.org/sites/default/files/misc/Coupled%20hidden%20markov%20model%20for%20electrocorticographic%20signal%20classification.pdf
-- "Coupled Hidden Markov Model for Electrocorticographic Signal Classification"

== Long Short Term Memory ==

http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
-- "Long Short-Term Memory"

ftp://ftp.idsia.ch/pub/juergen/2002_icannMusic.pdf
-- "Learning The Long-Term Structure of the Blues"

http://www.overcomplete.net/papers/nn2012.pdf
-- "A generalized LSTM-like training algorithm for second-order recurrent neural networks"

http://www.iro.umontreal.ca/~eckdoug/papers/2003_nn.pdf
-- "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets"

http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html
-- "Long Short-Term Memory dramatically improves Google Voice etc"

https://arxiv.org/pdf/1511.05552v4.pdf --
"Recurrent Neural Networks Hardware Implementation on FPGA"

http://vast.cs.ucla.edu/sites/default/files/publications/ASP-DAC2017-1352-11.pdf --
"FPGA-based Accelerator for Long Short-Term Memory Recurrent Neural Networks"

== Question Answering ==

http://www.overcomplete.net/papers/bica2012.pdf
-- "Neural Architectures for Learning to Answer Questions"

https://cs.umd.edu/~miyyer/pubs/2014_qb_rnn.pdf
-- "A Neural Network for Factoid Question Answering over Paragraphs"

http://arxiv.org/pdf/1502.05698.pdf
-- "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"

http://www.peterschueller.com/pub/2014/2014_winograd_schemas_relevance_knowledge_graphs.pdf
-- "Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs"

http://ijcai.org/papers15/Papers/IJCAI15-190.pdf
-- "Towards Addressing the Winograd Schema Challenge — Building and Using a Semantic Parser and a Knowledge Hunting Module"

http://arxiv.org/pdf/1506.05869v2.pdf
-- "A Neural Conversational Model"

http://arxiv.org/pdf/1508.05508v1.pdf
-- "Towards Neural Network-based Reasoning"

http://www.visualqa.org/vqa_iccv2015.pdf
-- "VQA: Visual Question Answering"

== Propagators ==
Cells must support three operations:
*add some content
*collect the content currently accumulated
*register a propagator to be notified when the accumulated content changes
*When new content is added to a cell, the cell must merge the addition with the content already present. When a propagator asks for the content of a cell, the cell must deliver a complete summary of the information that has been added to it.
*The merging of content must be commutative, associative, and idempotent. The behavior of propagators must be monotonic with respect to the lattice induced by the merge operation.
*http://web.mit.edu/~axch/www/art.pdf *http://groups.csail.mit.edu/mac/users/gjs/propagators/
*http://dustycloud.org/blog/sussman-on-ai/

== Boosting ==

http://www.cs.princeton.edu/courses/archive/spr08/cos424/readings/Schapire2003.pdf
-- "The Boosting Approach to Machine Learning An Overview"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.9955&rep=rep1&type=pdf
-- "Ensembling Neural Networks: Many Could Be Better Than All"

http://www.cs.columbia.edu/~rocco/Public/mlj9.pdf
-- "Random Classification Noise Defeats All Convex Potential Boosters"

== Support Vector Machines ==

http://research.microsoft.com/en-us/um/people/cburges/papers/svmtutorial.pdf
-- "A Tutorial on Support Vector Machines for Pattern Recognition"

== Wire Length / Small World Networks ==

http://koulakovlab.cshl.edu/publications/koulakov_chklovskii_2000.pdf
-- "A wire length minimization approach to ocular dominance patterns in mammalian visual cortex"

http://papers.nips.cc/paper/1910-foundations-for-a-circuit-complexity-theory-of-sensory-processing.pdf
-- "Foundations for a Circuit Complexity Theory of Sensory Processing"

https://www.nada.kth.se/~cjo/documents/small_world.pdf
-- "Small-World Connectivity and Attractor Neural Networks"

http://www.doc.ic.ac.uk/~mpsha/ShanahanPhysRevEPreprint.pdf
-- "The Dynamical Complexity of Small-World Networks of Spiking Neurons"

http://www.dam.brown.edu/people/elie/papers/small_world.pdf
-- "Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.4501&rep=rep1&type=pdf
-- "Transition from Random to Small-World Neural Networks by STDP Learning Rule"

http://tamar.tau.ac.il/~eshel/papers/J_Neural_Engineering.pdf
-- "Compact self-wiring in cultured neural networks"

== Backpropagation ==

http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf -- "Neural Networks - A Systematic Introduction"

http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf (works through simple example)

http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/

http://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf

also see http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf (entire book "Neural Networks - a Systemic Introduction" by Raul Rojas)

http://work.caltech.edu/lectures.html Hoeffding's inequality, VC Dimension and Back Propagation ANN

http://www.cs.stir.ac.uk/research/publications/techreps/pdf/TR148.pdf ("Learning XOR: exploring the space of a classic problem")

http://www.sysc.pdx.edu/classes/Werbos-Backpropagation%20through%20time.pdf
-- "Backpropagation Through Time: What it Does and How to Do It"

== Computer Vision ==

http://www.cv-foundation.org/openaccess/CVPR2015.py -- some interesting papers here

http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf -- "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"

== Visual Perception (Biological Systems) ==

http://cbcl.mit.edu/publications/ps/Serre_etal_PBR07.pdf
-- "A quantitative theory of immediate visual recognition"

http://www.dam.brown.edu/ptg/REPORTS/Invariance.pdf
-- "Invariance and Selectivity in the Ventral Visual Pathway"

http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf
-- "Hierarchical Bayesian inference in the visual cortex"

== Neural Synchrony ==

http://arxiv.org/pdf/1312.6115.pdf
-- "Neuronal Synchrony in Complex-Valued Deep Networks"

== Spiking Neural Networks ==

http://ncs.ethz.ch/projects/evospike/publications/ICONIP2011%20Springer%20LNCS%20Nutta.pdf -- "EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network"

http://dai.fmph.uniba.sk/~benus/confers/CNGM_ICANN05.pdf -- "Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques"

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3902 -- "Pattern Recognition in a Bucket"

http://www.igi.tugraz.at/maass/psfiles/221.pdf -- "Noise as a Resource for Computation and Learning in Spiking Neural Networks"

http://web.stanford.edu/group/brainsinsilicon/goals.html -- Neurogrid

http://apt.cs.manchester.ac.uk/projects/SpiNNaker/ -- SpiNNaker

http://personalpages.manchester.ac.uk/staff/javier.navaridas/pubs/2012.jpdc.pdf -- more on SpiNNaker

http://apt.cs.manchester.ac.uk/ftp/pub/apt/papers/JavN_ICS09.pdf -- "Understanding the Interconnection Network of SpiNNaker"

==Hierarchical Temporal Memory==

https://github.com/numenta/nupic/wiki/Hierarchical-Temporal-Memory-Theory

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000532 -- "Towards a Mathematical Theory of Cortical Micro-circuits"

==Distributed Neural Networks==

https://www.paypal-engineering.com/2015/01/12/deep-learning-on-hadoop-2-0-2/ -- paypal engineering blog, paper describes implementation of neural network as described by [http://www.maths.bris.ac.uk/~maxvd/reduce_dim.pdf Hinton 2006] on Hadoop

http://www.sciencemag.org/content/suppl/2006/08/04/313.5786.504.DC1/Hinton.SOM.pdf -- supplementary material for [http://www.maths.bris.ac.uk/~maxvd/reduce_dim.pdf Hinton 2006]

http://www.cs.otago.ac.nz/staffpriv/hzy/papers/pdcs03.pdf -- "Parallelization of a Backpropagation Neural Network on a Cluster Computer"

http://arxiv.org/pdf/1404.5997v2.pdf -- "One weird trick for parallelizing convolutional neural networks"

http://www.ics.uci.edu/~jmoorkan/pub/gpusnn-ijcnn.pdf -- "Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors"

==Mixture of Experts==

http://www.cs.toronto.edu/~fritz/absps/jjnh91.pdf -- "Adaptive Mixtures of Local Experts"

==Hopfield nets and RBMs==

http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf -- Yoshua Bengio, 2009, Learning Deep Architectures for AI

http://deeplearning.cs.cmu.edu/ -- Syllabus for cs course on deep learning, possible source of literature for the library

https://class.coursera.org/neuralnets-2012-001/lecture -- Lecture 11 - 15 to aid in understanding of Hinton 2006 paper above

http://www.pnas.org/content/79/8/2554.full.pdf -- "Neural networks and physical systems with emergent collective computational abilities" (Hopfield 1982)

http://page.mi.fu-berlin.de/rojas/neural/chapter/K13.pdf -- "The Hopfield Model" (Rojas 1996)

http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"

http://www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/cameraready_xwjiabibe.pdf -- "A Novel Semi-supervised Deep Learning Framework
for Affective State Recognition on EEG Signals"

http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf -- "A Practical Guide to Training Restricted Boltzmann Machines"

http://arxiv.org/pdf/1503.07793v2.pdf
-- "Gibbs Sampling with Low-Power Spiking Digital Neurons"

http://arxiv.org/pdf/1311.0190v1 -- "On the typical properties of inverse problems in statistical mechanics" Iacopo Mastromatteo 2013

http://jmlr.org/proceedings/papers/v5/salakhutdinov09a/salakhutdinov09a.pdf -- "Deep Boltzmann Machines" Salakhutdinov & Hinton 2009

http://www.cs.toronto.edu/~hinton/absps/tr00-004.pdf -- "Training Products of Experts by Minimizing Contrastive Divergence"

http://www.eecg.toronto.edu/~pc/research/publications/ly.fpga2009.submitted.pdf -- "A High-Performance FPGA Architecture for Restricted
Boltzmann Machines" Ly & Chow 2009

https://pdfs.semanticscholar.org/85fa/f7c3c05388e2bcd097a416606bdd88fc0c7c.pdf -- "A MULTI-FPGA ARCHITECTURE FOR STOCHASTIC RESTRICTED BOLTZMANN MACHINES" Ly & Chow 2009

== Variational Renormalization ==

https://arxiv.org/pdf/1410.3831 -- "An exact mapping between the Variational Renormalization Group and Deep Learning"

== Neuromorphic Stuff ==

https://arxiv.org/pdf/1508.01008.pdf -- "INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks" Chung, Shin & Kang 2015

== Markov Chain Monte Carlo ==

http://www.cs.princeton.edu/courses/archive/spr06/cos598C/papers/AndrieuFreitasDoucetJordan2003.pdf
-- "An Introduction to MCMC for Machine Learning"

http://jmlr.org/proceedings/papers/v37/salimans15.pdf
-- "Markov Chain Monte Carlo and Variational Inference: Bridging the Gap"

https://www.umiacs.umd.edu/~resnik/pubs/LAMP-TR-153.pdf -- "Gibbs Sampling for the Uninitiated"

==Entrainment==

http://www.scirp.org/Journal/PaperDownload.aspx?paperID=33251 -- "Alpha Rhythms Response to 10 Hz Flicker Is Wavelength Dependent"

http://www.brainmachine.co.uk/wp-content/uploads/Herrmann_Flicker.pdf -- "EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena"

http://www.jneurosci.org/content/23/37/11621.full.pdf -- "Human Cerebral Activation during Steady-State Visual-Evoked Responses"

http://www.dauwels.com/Papers/CogDyn%202009.pdf -- "On the synchrony of steady state visual evoked potentials and oscillatory burst events"

https://www.tu-ilmenau.de/fileadmin/public/lorentz-force/publications/peer/2012/haueisen2012/Halbleib_JCN_2012_Topographic_analysis_photic_driving.pdf -- "Topographic Analysis of Engagement and Disengagement of Neural Oscillators in Photic Driving: A Combined Electroencephalogram/Magnetoencephalogram Study"

==Mining Scientific Literature==

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC153503/pdf/1471-2105-4-11.pdf -- "PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine" Donaldson 2003 BMC Bioinformatics

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674139/pdf/pcbi.1004630.pdf -- "Text Mining for Protein Docking" Badal 2015 PLoS Comput Biol.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4691339/pdf/bav116.pdf -- "Biocuration with insufficient resources and fixed timelines" Rodriguez-Esteban 2015 Database: The Journal of Biological Databases and Curation

==(not necessarilly very) Current Discussion==

re Tononi's "Integrated Information Theory" http://www.scottaaronson.com/blog/?p=1799

(19 February 2014) starting to think about possibility for experiments (loosely) related to [https://en.wikipedia.org/wiki/Visual_evoked_potential Visual Evoked Potential] research again - for instance:

http://www.biosemi.com/publications/pdf/Bierman.pdf -- Wave function collapse

[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999

[[File:CoherentEEGAmbiguousFigureBinding.pdf]] -- "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" -- Klemm, Li, and Hernandez 2000

Note these two papers flog coherence measures - not trying to focus so much on that analysis right now, more interested in general understanding of what these experiments are about with possible goal of designing simpler experiments & analysis of similar perceptual/cognitive phenomena.

Here is an article that looks more directly at visual evoked potential measures:

[[File:ERP_Stereoscopic.pdf]] -- "Evoked Responses to Distinct and Nebulous Stereoscopic Stimuli" -- Dunlop et al 1983

(11 September 2013) more on analysis methods:

http://slesinsky.org/brian/misc/eulers_identity.html

http://www.dspguide.com/ch8/1.htm

[[File:Fftw3.pdf]]

[[File:ParametricEEGAnalysis.pdf]]

[[File:ICATutorial.pdf]]

[[File:ICAFrequencyDomainEEG.pdf]]

(21 August 2013) - readings relating statistical (etc math / signal processing / pattern recognition / machine learning) methods for EEG data interpretation. A lot of stuff, a bit of nonsense ... and ... statistics!

Would be good to identify any papers suitable for more in-depth study. Currently have a wide field to graze for selections:

[[File:DWTandFFTforEEG.pdf]] "EEG Classifier using Fourier Transform and Wavelet Transform" -- Maan Shaker, 2007

[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999

[[File:KulaichevCoherence.pdf]] -- "The Informativeness of Coherence Analysis in EEG Studies" -- A. P. Kulaichev 2009 ''note: interesting critical perspective re limitations, discussion of alternative analytics''

[[File:ContinuousAndDiscreteWaveletTransforms.pdf]] -- review of (pre-1990) wavelet literature -- Christopher Heil and David Walnut, 1989

[[File:EEGGammaMeditation.pdf]] -- "Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self" -- Dietrich Lehman et al 2001

http://neuro.hut.fi/~pavan/home/Hyvarinen2010_FourierICA_Neuroimage.pdf - "Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis" -- Aapo Hyvarinen, Pavan Ramkumar, Lauri Parkkonen, Riitta Hari - paper published in Neuroimage vol 49 (2010)

----

[http://www.nickgillian.com/software/grt OpenSource Machine Learning Algs from NG @MIT]
 [https://www.usenix.org/system/files/conference/usenixsecurity12/sec12-final56.pdf Consumer grade EEG used to see "P300" reponse] and for thoes with a short attention span [http://www.extremetech.com/extreme/134682-hackers-backdoor-the-human-brain-successfully-extract-sensitive-data tldr]
 (discussed at meetup Wednesday 31 July 2013)
 "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" Klemm, Li, and Hernandez 2000
 [[File:CoherentEEGAmbiguousFigureBinding.pdf]]
 "We tested the hypothesis that perception of an alternative image in ambiguous figures would be manifest as high-frequency (gamma) components that become synchronized over multiple scalp sites as a "cognitive binding" process occurs."
 

----

[http://dreamsessions.net art, dream, and eeg]

----

[http://www.believermag.com/issues/200710/?read=article_aviv mind v brain, hobson v solms]
 http://en.wikipedia.org/wiki/Activation-synthesis_hypothesis
 [[File:HobsomREMDreamProtoconsciousness.pdf|Hobson09ProtosconsciousnessREMDream]]

"Hobson and McCarley originally proposed in the 1970s that the differences in the waking-NREM-REM sleep cycle was the result of interactions between aminergic REM-off cells and cholinergic REM-on cells.[4] This was perceived as the activation-synthesis model, stating that brain activation during REM sleep results in synthesis of dream creation.[1][1] Hobson's five cardinal characteristics include: intense emotions, illogical content, apparent sensory impressions, uncritical acceptance of dream events, and difficulty in being remembered."

----

Berkeley Labs

[http://gallantlab.org/index.html Gallant Group]
 [http://walkerlab.berkeley.edu/ Walker Group]
 [http://socrates.berkeley.edu/~plab/ Palmer Group]

==Sleep Research==

[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2335403/ Comment on the AASM Manual for the Scoring of Sleep and Associated Events]

==random tangents==
(following previous discussion) - we might select a few to study in more depth
(... or not! Plenty more to explore - suggestions (random or otherwise) are welcome.
http://www.meltingasphalt.com/neurons-gone-wild/ --
Neurons Gone Wild - Levels of agency in the brain.

'''stereoscopic perception:'''
*[[File:ERP_Stereoscopic.pdf]]

some (maybe) interesting background on Information Theory (cool title...)
Claude Shannon: "Communication in the Presence of Noise"
[[File:Shannon_noise.pdf]]
"We will call a system that transmits without errors at the rate ''C'' an ideal system.
Such a system cannot be achieved with any finite encoding process
but can be approximated as closely as desired."

wikipedia etc quick reads:
https://en.wikipedia.org/wiki/Eeg
https://en.wikipedia.org/wiki/Neural_synchronization
https://en.wikipedia.org/wiki/Event-related_potentials
http://www.scholarpedia.org/article/Spike-and-wave_oscillations
http://www.scholarpedia.org/article/Thalamocortical_oscillations

==Previously==

[http://www.psychiclab.net/ Masahiro's EEG Device/IBVA Software]

[http://www.instructables.com/id/open-brain-wave-interface-hardware-1/ and ... open source hardware design and kits on instructables.com]

[http://brainstorms.puzzlebox.info/ Puzzlebox - Opensource BCI Developers]

Morgan from GazzLab @ MissionBay/UCSF

https://github.com/gazzlab

Let's ease into a lightweight "journal club" discussion with this technical report from NeuroSky.

Name: A user-friendly SSVEP-based brain-computer interface using a time-domain classifier, Luo A and Sullivan TJ 2010

URL: [[File:NeuroSkyVEP.pdf‎]]

Please add your comments & questions here.

==Background Reading==

http://nanosouffle.net/ (view into Arxiv.org)

Name: Hunting for Meaning after Midnight, Miller 2007

URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0002.pdf>

Name: Broken mirrors, Ram, VS, & Oberman, LM, 2006, Nov

URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0003.pdf>

Ramachandran Critique

http://blogs.scientificamerican.com/guest-blog/2012/11/06/whats-so-special-about-mirror-neurons/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773693/

Sleep/Dream Studies

http://www.cns.atr.jp/dni/en/publications/

==NeuroSky Docs==
[[File:NeuroSkyDongleProtocol.pdf‎]]

[[File:NeuroSkyCommunicationsProtocol.pdf‎]]

==Android Neutral Network Fuzzy Learning app==
[https://play.google.com/store/apps/details?id=com.faadooengineers.free_neuralnetworkandfuzzysystems Android Neutral Network Fuzzy Learning app in Play Store]

==Learning about Neural Networks==
* What type of network? [http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma RMB (Restricted Boltzmann Machine) vs Autoencoder/MLP vs CNN (Convolutional Neural Networks)]
* Andrej Karpathy's [http://cs.stanford.edu/people/karpathy/convnetjs/ Convolutional Neural Network coded in JavaScript (ConvNetJS)]
* Andrej Karpathy's [http://karpathy.github.io/2015/10/25/selfie/ What a Deep Neural Network thinks about your #selfie (background on Convolutional Neural Networks for image recognition and classification)]
* [https://blog.webkid.io/neural-networks-in-javascript/ Neural Networks in JavaScript w/MNIST]
* [http://www.antoniodeluca.info/blog/10-08-2016/neural-networks-in-javascript.html Another NN in JS]
* [http://caza.la/synaptic/ The Synaptic "architecture-free" neural network library in JS]

DreamTeam/Reading

2018-03-15T06:05:20Z

192.195.80.12: Generative Adversarial Networks

(note wiki contains some useful clues re previous neuro research at Noisebridge ... For example, the [[Analog_EEG_Amp]] page describes some project ideas and work done by others here in 2012)

https://noisebridge.net/wiki/NBDSM -- noiseBridge Deepnet and Statistical Mechanics -- first meetup @ noisebridge 7/6/17 at 7PM

https://metacademy.org/
-- machine learning knowledge graph

https://machinelearningguide.libsyn.com/rss -- machine learning guide podcast

http://www.thetalkingmachines.com/ -- podcast

https://karpathy.github.io/2015/05/21/rnn-effectiveness/

http://alexandre.barachant.org/papers/

http://ncs.ethz.ch/publications -- neuromorphic cognitive systems

https://github.com/crillab/gophersat/blob/master/examples/sat-for-noobs.md -- SAT solvers

https://media.ccc.de/v/34c3-8948-low_cost_non-invasive_biomedical_imaging -- Open EIT 34c3 talk https://github.com/OpenEIT

http://acrovirt.org/ -- sensors

http://www.neuroeducate.com/ -- citizen neuroscience

https://www.youtube.com/watch?v=9mZuyUzyN4Q -- "Categories for the Working Hacker"

http://radicalsciencenews.org/599-2/ -- "Deep Learning Fuels Nvidia’s Self-Driving Car Technology"

== Generative Adversarial Networks (GAN) ==

https://arxiv.org/pdf/1710.08864 -- "One pixel attack for fooling deep neural networks"

== OpenCV ==

http://arnab.org/blog/so-i-suck-24-automating-card-games-using-opencv-and-python

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.585&rep=rep1&type=pdf -- "Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm"

== Category Theory ==

https://arxiv.org/pdf/1711.10455 -- "Backprop as Functor: A compositional perspective on supervised learning"

== Proof Searcher ==

https://arxiv.org/pdf/cs/0207097 -- "Optimal Ordered Problem Solver"

http://people.idsia.ch/~juergen/ultimatecognition.pdf -- "Ultimate Cognition a la Gödel"

http://people.idsia.ch/~juergen/selfreflection.pdf -- "Towards an Actual Gödel Machine Implementation"

== Capsule Models ==

https://arxiv.org/pdf/1710.09829.pdf -- "Dynamic Routing Between Capsules"

https://openreview.net/pdf?id=HJWLfGWRb -- "Matrix Capsules with EM Routing"

== Infrared Neuroimaging ==

http://www.ecse.rpi.edu/~yazici/bio_book.pdf -- "Near Infrared Imaging and Spectroscopy for Brain Activity Monitoring"

== Geometry ==

http://arxiv.org/abs/1710.10784 -- "How deep learning works --The geometry of deep learning"

== Affective Computing ==

http://affect.media.mit.edu/pdfs/05.ahn-picard-acii.pdf -- "Affective-Cognitive Learning and Decision
Making: A Motivational Reward Framework For Affective Agents"

== Explainability ==

http://arxiv.org/abs/1708.01785 -- "Interpreting CNN knowledge via an Explanatory Graph"

== NLP ==

https://arxiv.org/pdf/1605.06640 -- "Programming with a Differentiable Forth Interpreter"

https://pdfs.semanticscholar.org/f683/dbe8a22d633ad3a2cff379b055b26684a838.pdf -- "Solving General Arithmetic Word Problems"

https://arxiv.org/pdf/1611.04558.pdf -- "Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation"

http://emnlp2014.org/papers/pdf/EMNLP2014162.pdf -- "GloVe: Global Vectors for Word Representation"

== RNNs ==

https://arxiv.org/pdf/1611.01576.pdf -- "Quasi Recurrent Neural Networks"

== Hyper-parameter Optimization ==

https://arxiv.org/abs/1603.06560 -- "Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization"

== Transfer Learning ==

http://arxiv.org/abs/1710.10776v1 -- "Transfer Learning to Learn with Multitask Neural Model Search"

== Reinforcement Learning ==

http://www2.hawaii.edu/~sstill/StillPrecup2011.pdf -- "An information-theoretic approach to curiosity-driven reinforcement learning"

https://arxiv.org/abs/1605.06676 -- "Learning to Communicate with Deep Multi-Agent Reinforcement Learning"

== Learning to Learn ==

https://arxiv.org/pdf/1703.01041.pdf -- "Large-Scale Evolution of Image Classifiers"

https://arxiv.org/pdf/1611.01578 -- "Neural Architecture Search with Reinforcement Learning"

== The Utility of "Noise" in ML ==

https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf -- "Dropout: A Simple Way to Prevent Neural Networks from Overfitting"

http://yann.lecun.com/exdb/publis/pdf/lecun-90b.pdf -- "Optimal Brain Damage"

https://arxiv.org/pdf/1502.01852.pdf -- "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"

== One-shot learning ==

https://arxiv.org/abs/1605%2E06065 -- "One-shot Learning with Memory-Augmented Neural Networks"

== Program Synthesis ==

https://pdfs.semanticscholar.org/0163/35ce7e0a073623e1deac7138b28913dbf594.pdf -- "Human-level concept learning through probabilistic program induction"

https://arxiv.org/pdf/1511.06279.pdf -- "Neural Programmer: Inducing Latent Programs with Gradient Descent"

https://arxiv.org/abs/1608.04428 -- "TerpreT: A Probabilistic Programming Language for Program Induction" Gaunt et al 2016

== Machine Learning Interaction ==

https://teachablemachine.withgoogle.com/#

== Game Theory ==

https://arxiv.org/abs/1707.01068v1 - Maintaining cooperation in complex social dilemmas using deep reinforcement learning

== Questions of Physics and Free Will ==

http://www.scottaaronson.com/papers/giqtm3.pdf - The Ghost in the Quantum Turing Machine

== CNN ==

https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/ - "A Beginner's Guide To Understanding Convolutional Neural Networks"

https://adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/ - "A Beginner's Guide To Understanding Convolutional Neural Networks Part 2"

http://scs.ryerson.ca/~aharley/vis/harley_vis_isvc15.pdf -- "An Interactive Node-Link Visualization
of Convolutional Neural Networks"

http://white.stanford.edu/teach/index.php/An_Introduction_to_Convolutional_Neural_Networks

http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf -- "Learning to Generate Chairs With Convolutional Neural Networks"

http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf
-- "What's Wrong With Deep Learning?"

== Mind-Body Relations ==

http://www.pnas.org/content/111/20/7379.full.pdf -- "Voluntary activation of the sympathetic nervous system and attenuation of the innate immune response in humans"

== Math ==

https://arxiv.org/pdf/1311.1090.pdf -- "Polyhedrons and Perceptrons Are Functionally Equivalent"

Example code and training data using polyhedrons developed by author of above paper: https://www.noisebridge.net/wiki/DreamTeam#Code

== Bayesian Inference ==

https://noisebridge.net/images/e/ef/Perception_is_in_the_Details12.pdf --
"Perception is in the Details: A Predictive Coding Account of the Psychedelic Phenomenon"

http://rsif.royalsocietypublishing.org/content/10/86/20130475 --
"Life as we know it"

http://jmlr.csail.mit.edu/proceedings/papers/v31/wang13b.pdf --
"Collapsed Variational Bayesian Inference for Hidden Markov Models"

http://www.datalab.uci.edu/papers/nips06_cvb.pdf --
"A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation"

http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf --
"Hierarchical Bayesian inference in the visual cortex"

https://www.researchgate.net/profile/Til_Bergmann/publication/262423308_Temporal_coding_organized_by_coupled_alpha_and_gamma_oscillations_prioritize_visual_processing/links/0deec537d1bfda474c000000/Temporal-coding-organized-by-coupled-alpha-and-gamma-oscillations-prioritize-visual-processing.pdf --
"Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing"

http://www.cell.com/neuron/pdf/S0896-6273(15)00823-5.pdf --
"Rhythms for Cognition: Communication through Coherence"

http://www.biorxiv.org/content/biorxiv/early/2014/05/06/004804.full.pdf --
"Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels"

== Speech Recognition ==

https://arxiv.org/pdf/1612.00694v1 -- "ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA"

== Sound Classification ==

https://arxiv.org/pdf/1608.04363v2 -- "Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification"

https://arxiv.org/pdf/1605.09507 "Deep convolutional neural networks for predominant instrument recognition in polyphonic music"

== Hardware Implementations - FPGA, GPU, etc ==

https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf --
"Artificial neural networks in hardware: A survey of two decades of progress"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.133.9185&rep=rep1&type=pdf
"A Self-Repairing Multiplexer-Based FPGA Inspired by Biological Processes"

http://www.genetic-programming.com/jkpdf/fpga1998.pdf -- "Evolving Computer Programs using Rapidly Reconfigurable Field-Programmable Gate Arrays and Genetic Programming"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.2588&rep=rep1&type=pdf -- "Flexible Implementation of Genetic Algorithms on FPGAs"

http://www.users.muohio.edu/jamiespa/html_papers/gem_10.pdf -- "Revisiting Genetic Algorithms for the FPGA Placement Problem"

https://arxiv.org/pdf/1609.09296v1 -- "Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.7533&rep=rep1&type=pdf -- "FPGA-TARGETED NEURAL ARCHITECTURE FOR EMBEDDED ALERTNESS DETECTION"

https://arxiv.org/pdf/1611.02450v1 -- "PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks"

https://arxiv.org/pdf/1605.06402v1 -- "Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks"

https://homes.cs.washington.edu/~luisceze/publications/snnap-hpca-2015.pdf -- "SNNAP: Approximate Computing on Programmable SoCs via Neural Acceleration"

https://arxiv.org/pdf/1701.00485v2 -- "Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices"

== VLSI ==

http://ncs.ethz.ch/pubs/pdf/Indiveri_etal06.pdf -- "A VLSI array of low-power spiking neurons and bistable synapses with spike–timing dependent plasticity"

== Pruning ==

http://papers.nips.cc/paper/5784-learning-both-weights-and-connections-for-efficient-neural-network.pdf --
"Learning both Weights and Connections for Efficient Neural Networks"

https://arxiv.org/pdf/1701.04465 -- "The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning"

https://arxiv.org/pdf/1512.08571 -- "Structured Pruning of Deep Convolutional Neural Networks"

https://arxiv.org/pdf/1611.01427 -- "Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks"

== Efficient Neural Networks via Compression, Quantization, Model Reduction, etc ==

https://arxiv.org/pdf/1504.04788 -- "Compressing Neural Networks with the Hashing Trick"

https://arxiv.org/pdf/1509.08745 -- "Compression of Deep Neural Networks on the Fly"

https://arxiv.org/pdf/1502.03436 -- "An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections"

https://arxiv.org/pdf/1510.00149 -- "Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding"

https://arxiv.org/pdf/1612.00891 -- "Parameter Compression of Recurrent Neural Networks and Degradation of Short-term Memory"

https://arxiv.org/pdf/1609.07061 -- "Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations"

https://arxiv.org/pdf/1607.05418 -- "Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off"

https://arxiv.org/pdf/1602.08194 -- "Scalable and Sustainable Deep Learning via Randomized Hashing"

https://arxiv.org/pdf/1508.05463 -- "StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity"

https://arxiv.org/pdf/1412.7024 -- "Training Deep Neural Networks with Low Precision Multiplications"

https://arxiv.org/pdf/1612.03940 -- "Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks"

https://arxiv.org/pdf/1609.00222 -- "Ternary Neural Networks for Resource-Efficient AI Applications"

== Neural Network Hyperparameter Optimization ==

https://arxiv.org/pdf/1601.00917 -- "DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks"

== Neural Network based EEG Analysis ==
end
http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"

http://inter-eng.upm.ro/2012/files/proceedings/papers/paper72.pdf --
"Neural Network Parallelization on FPGA Platform for EEG Signal Classification"

== Seizure Detection ==

also see https://noisebridge.net/wiki/Kaggle for a (September 2016) current project!

and https://github.com/kevinjos/kaggle-aes-seizure-prediction (some earlier exploration, November 2014)

(broken link, sorry) http://www.sersc.org/journals/ijsip/vol7_no5/26.pdf --
"A Neural Network Model for Predicting Epileptic Seizures based on Fourier-Bessel Functions"

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf --
"A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"

(another broken link) http://cs.uni-muenster.de/Professoren/Lippe/diplomarbeiten/html/eisenbach/Untersuchte%20Artikel/PPHD+00.pdf --
"Recurrent neural network based preenddiction of epileptic seizures in intra- and extracranial EEG"

== Visible Light Sensor Network ==

http://infoteh.etf.unssa.rs.ba/zbornik/2016/radovi/KST-1/KST-1-15.pdf --
"Analysis of Visible Light Communication System for Implementation in Sensor Networks"

== Neurophysiology ==

http://www.buzsakilab.com/content/PDFs/BuzsakiKoch2012.pdf -- "The origin of extracellular fields and
currents — EEG, ECoG, LFP and spikes"

== Signal Processing ==

http://provideyourown.com/2011/analogwrite-convert-pwm-to-voltage/ -- "Arduino’s AnalogWrite – Converting PWM to a Voltage"

http://sim.okawa-denshi.jp/en/PWMtool.php -- "RC Low-pass Filter Design for PWM (Transient Analysis Calculator)"

== Hyperdimensional Computing ==

http://redwood.berkeley.edu/pkanerva/papers/kanerva09-hyperdimensional.pdf --
"Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors"

http://arxiv.org/pdf/1602.03032.pdf --
"Associative Long Short-Term Memory"

== Bird Flocks and Maximum Entropy ==

http://arxiv.org/pdf/1107.0604v1 --
"Statistical Mechanics and Flocks of Birds"

http://arxiv.org/pdf/1307.5563v1 --
"Social interactions dominate speed control in driving natural flocks toward criticality"

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_penfield.pdf --
"Information and Entropy (Course Notes)"

== Whale Songs ==

https://arxiv.org/pdf/1307.0589.pdf -- "The Orchive : Data mining a massive bioacoustic archive"

https://www.researchgate.net/profile/Herbert_Roitblat/publication/13429327_The_neural_network_classification_of_false_killer_whale_%28Pseudorca_crassidens%29_vocalizations/links/540d2ff60cf2df04e75478cd.pdf -- "The neural network classification of false killer whale (Pseudorca crassidens) vocalizations"

http://users.iit.demokritos.gr/~paliourg/papers/PhD.pdf -- "REFINEMENT OF TEMPORAL CONSTRAINTS IN AN EVENT RECOGNITION SYSTEM USING SMALL DATASETS"

https://www.nersc.no/sites/www.nersc.no/files/master_thesis_sebastian_menze.pdf -- "Estimating fin whale distribution from ambient noise spectra using Bayesian inversion"

http://sis.univ-tln.fr/~glotin/IJCNN2015_IHMMbioac_BartChamGlot.pdf -- "Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis"

https://www.inf.ed.ac.uk/publications/thesis/online/IM030057.pdf -- "Hidden Markov Model Clustering of Acoustic Data"

http://danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/ -- Using deep learning to listen for whales

== Computational Cognitive Neuroscience ==

http://www.pnas.org/content/110/41/16390.full -- "Indirection and symbol-like processing in the prefrontal cortex and basal ganglia"

http://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/docs/jaf.pdf -- "Connectionism and Cognitive Architecture: A Critical Analysis"

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2728678/pdf/nihms131814.pdf -- Neves et al 2008 "Cell Shape and Negative Links in Regulatory Motifs Together Control Spatial Information Flow in Signaling Networks"

http://psych.colorado.edu/~oreilly/papers/AisaMingusOReilly08.pdf -- "The Emergent Neural Modeling System"

https://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators

== Text Generation ==

http://www.cs.toronto.edu/~ilya/pubs/2011/LANG-RNN.pdf -- "Generating Text with Recurrent Neural Networks"

== Games ==

http://setgame.com/sites/default/files/teacherscorner/COGNITIVE%20MODELING%20WITH%20SET.pdf -- "How to Construct a Believable Opponent using Cognitive Modeling in the Game of Set"

http://www-personal.umich.edu/~charchan/SET.pdf -- "SETs and Anti-SETs: The Math Behind the Game of SET"

http://personal.plattsburgh.edu/quenelgt/talks/set.pdf -- "Introduction to Set"

http://web.engr.illinois.edu/~pbg/papers/set.pdf -- "On the Complexity of the Game of Set"

http://www.warwick.ac.uk/staff/D.Maclagan/papers/set.pdf -- "The Card Game Set"

http://www.math.ucdavis.edu/~anne/FQ2011/set_game.pdf -- "The Game Set"

https://www.youtube.com/watch?v=k2rgzZ2WXKo -- "Best Practices for Procedural Narrative Generation" Chris Martens

== Large Scale Brain Simulation ==

http://www.nowere.net/b/arch/96550/src/1378907656268.pdf -- "A world survey of artificial brain projects, Part I: Large-scale brain simulations"

== Music ==

http://cmr.soc.plymouth.ac.uk/publications/bci-wkshop.pdf -- "ON GENERATING EEG FOR CONTROLLING MUSICAL SYSTEMS"

== Code ==

https://github.com/nbdt/gotrain (our ANN code)

https://github.com/nbdt/openbci-golang-server (EEG data acquisition and visualization software for OpenBCI)

== Hidden Markov Models ==

http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf
-- "A Revealing Introduction to Hidden Markov Models"

http://www.jelmerborst.nl/pubs/Borst2013b.pdf
-- "Discovering Processing Stages by combining EEG with Hidden Markov Models"

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf
-- "A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"

http://www.schalklab.org/sites/default/files/misc/Coupled%20hidden%20markov%20model%20for%20electrocorticographic%20signal%20classification.pdf
-- "Coupled Hidden Markov Model for Electrocorticographic Signal Classification"

== Long Short Term Memory ==

http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
-- "Long Short-Term Memory"

ftp://ftp.idsia.ch/pub/juergen/2002_icannMusic.pdf
-- "Learning The Long-Term Structure of the Blues"

http://www.overcomplete.net/papers/nn2012.pdf
-- "A generalized LSTM-like training algorithm for second-order recurrent neural networks"

http://www.iro.umontreal.ca/~eckdoug/papers/2003_nn.pdf
-- "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets"

http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html
-- "Long Short-Term Memory dramatically improves Google Voice etc"

https://arxiv.org/pdf/1511.05552v4.pdf --
"Recurrent Neural Networks Hardware Implementation on FPGA"

http://vast.cs.ucla.edu/sites/default/files/publications/ASP-DAC2017-1352-11.pdf --
"FPGA-based Accelerator for Long Short-Term Memory Recurrent Neural Networks"

== Question Answering ==

http://www.overcomplete.net/papers/bica2012.pdf
-- "Neural Architectures for Learning to Answer Questions"

https://cs.umd.edu/~miyyer/pubs/2014_qb_rnn.pdf
-- "A Neural Network for Factoid Question Answering over Paragraphs"

http://arxiv.org/pdf/1502.05698.pdf
-- "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"

http://www.peterschueller.com/pub/2014/2014_winograd_schemas_relevance_knowledge_graphs.pdf
-- "Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs"

http://ijcai.org/papers15/Papers/IJCAI15-190.pdf
-- "Towards Addressing the Winograd Schema Challenge — Building and Using a Semantic Parser and a Knowledge Hunting Module"

http://arxiv.org/pdf/1506.05869v2.pdf
-- "A Neural Conversational Model"

http://arxiv.org/pdf/1508.05508v1.pdf
-- "Towards Neural Network-based Reasoning"

http://www.visualqa.org/vqa_iccv2015.pdf
-- "VQA: Visual Question Answering"

== Propagators ==
Cells must support three operations:
*add some content
*collect the content currently accumulated
*register a propagator to be notified when the accumulated content changes
*When new content is added to a cell, the cell must merge the addition with the content already present. When a propagator asks for the content of a cell, the cell must deliver a complete summary of the information that has been added to it.
*The merging of content must be commutative, associative, and idempotent. The behavior of propagators must be monotonic with respect to the lattice induced by the merge operation.
*http://web.mit.edu/~axch/www/art.pdf *http://groups.csail.mit.edu/mac/users/gjs/propagators/
*http://dustycloud.org/blog/sussman-on-ai/

== Boosting ==

http://www.cs.princeton.edu/courses/archive/spr08/cos424/readings/Schapire2003.pdf
-- "The Boosting Approach to Machine Learning An Overview"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.9955&rep=rep1&type=pdf
-- "Ensembling Neural Networks: Many Could Be Better Than All"

http://www.cs.columbia.edu/~rocco/Public/mlj9.pdf
-- "Random Classification Noise Defeats All Convex Potential Boosters"

== Support Vector Machines ==

http://research.microsoft.com/en-us/um/people/cburges/papers/svmtutorial.pdf
-- "A Tutorial on Support Vector Machines for Pattern Recognition"

== Wire Length / Small World Networks ==

http://koulakovlab.cshl.edu/publications/koulakov_chklovskii_2000.pdf
-- "A wire length minimization approach to ocular dominance patterns in mammalian visual cortex"

http://papers.nips.cc/paper/1910-foundations-for-a-circuit-complexity-theory-of-sensory-processing.pdf
-- "Foundations for a Circuit Complexity Theory of Sensory Processing"

https://www.nada.kth.se/~cjo/documents/small_world.pdf
-- "Small-World Connectivity and Attractor Neural Networks"

http://www.doc.ic.ac.uk/~mpsha/ShanahanPhysRevEPreprint.pdf
-- "The Dynamical Complexity of Small-World Networks of Spiking Neurons"

http://www.dam.brown.edu/people/elie/papers/small_world.pdf
-- "Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.4501&rep=rep1&type=pdf
-- "Transition from Random to Small-World Neural Networks by STDP Learning Rule"

http://tamar.tau.ac.il/~eshel/papers/J_Neural_Engineering.pdf
-- "Compact self-wiring in cultured neural networks"

== Backpropagation ==

http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf -- "Neural Networks - A Systematic Introduction"

http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf (works through simple example)

http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/

http://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf

also see http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf (entire book "Neural Networks - a Systemic Introduction" by Raul Rojas)

http://work.caltech.edu/lectures.html Hoeffding's inequality, VC Dimension and Back Propagation ANN

http://www.cs.stir.ac.uk/research/publications/techreps/pdf/TR148.pdf ("Learning XOR: exploring the space of a classic problem")

http://www.sysc.pdx.edu/classes/Werbos-Backpropagation%20through%20time.pdf
-- "Backpropagation Through Time: What it Does and How to Do It"

== Computer Vision ==

http://www.cv-foundation.org/openaccess/CVPR2015.py -- some interesting papers here

http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf -- "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"

== Visual Perception (Biological Systems) ==

http://cbcl.mit.edu/publications/ps/Serre_etal_PBR07.pdf
-- "A quantitative theory of immediate visual recognition"

http://www.dam.brown.edu/ptg/REPORTS/Invariance.pdf
-- "Invariance and Selectivity in the Ventral Visual Pathway"

http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf
-- "Hierarchical Bayesian inference in the visual cortex"

== Neural Synchrony ==

http://arxiv.org/pdf/1312.6115.pdf
-- "Neuronal Synchrony in Complex-Valued Deep Networks"

== Spiking Neural Networks ==

http://ncs.ethz.ch/projects/evospike/publications/ICONIP2011%20Springer%20LNCS%20Nutta.pdf -- "EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network"

http://dai.fmph.uniba.sk/~benus/confers/CNGM_ICANN05.pdf -- "Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques"

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3902 -- "Pattern Recognition in a Bucket"

http://www.igi.tugraz.at/maass/psfiles/221.pdf -- "Noise as a Resource for Computation and Learning in Spiking Neural Networks"

http://web.stanford.edu/group/brainsinsilicon/goals.html -- Neurogrid

http://apt.cs.manchester.ac.uk/projects/SpiNNaker/ -- SpiNNaker

http://personalpages.manchester.ac.uk/staff/javier.navaridas/pubs/2012.jpdc.pdf -- more on SpiNNaker

http://apt.cs.manchester.ac.uk/ftp/pub/apt/papers/JavN_ICS09.pdf -- "Understanding the Interconnection Network of SpiNNaker"

==Hierarchical Temporal Memory==

https://github.com/numenta/nupic/wiki/Hierarchical-Temporal-Memory-Theory

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000532 -- "Towards a Mathematical Theory of Cortical Micro-circuits"

==Distributed Neural Networks==

https://www.paypal-engineering.com/2015/01/12/deep-learning-on-hadoop-2-0-2/ -- paypal engineering blog, paper describes implementation of neural network as described by [http://www.maths.bris.ac.uk/~maxvd/reduce_dim.pdf Hinton 2006] on Hadoop

http://www.sciencemag.org/content/suppl/2006/08/04/313.5786.504.DC1/Hinton.SOM.pdf -- supplementary material for [http://www.maths.bris.ac.uk/~maxvd/reduce_dim.pdf Hinton 2006]

http://www.cs.otago.ac.nz/staffpriv/hzy/papers/pdcs03.pdf -- "Parallelization of a Backpropagation Neural Network on a Cluster Computer"

http://arxiv.org/pdf/1404.5997v2.pdf -- "One weird trick for parallelizing convolutional neural networks"

http://www.ics.uci.edu/~jmoorkan/pub/gpusnn-ijcnn.pdf -- "Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors"

==Mixture of Experts==

http://www.cs.toronto.edu/~fritz/absps/jjnh91.pdf -- "Adaptive Mixtures of Local Experts"

==Hopfield nets and RBMs==

http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf -- Yoshua Bengio, 2009, Learning Deep Architectures for AI

http://deeplearning.cs.cmu.edu/ -- Syllabus for cs course on deep learning, possible source of literature for the library

https://class.coursera.org/neuralnets-2012-001/lecture -- Lecture 11 - 15 to aid in understanding of Hinton 2006 paper above

http://www.pnas.org/content/79/8/2554.full.pdf -- "Neural networks and physical systems with emergent collective computational abilities" (Hopfield 1982)

http://page.mi.fu-berlin.de/rojas/neural/chapter/K13.pdf -- "The Hopfield Model" (Rojas 1996)

http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"

http://www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/cameraready_xwjiabibe.pdf -- "A Novel Semi-supervised Deep Learning Framework
for Affective State Recognition on EEG Signals"

http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf -- "A Practical Guide to Training Restricted Boltzmann Machines"

http://arxiv.org/pdf/1503.07793v2.pdf
-- "Gibbs Sampling with Low-Power Spiking Digital Neurons"

http://arxiv.org/pdf/1311.0190v1 -- "On the typical properties of inverse problems in statistical mechanics" Iacopo Mastromatteo 2013

http://jmlr.org/proceedings/papers/v5/salakhutdinov09a/salakhutdinov09a.pdf -- "Deep Boltzmann Machines" Salakhutdinov & Hinton 2009

http://www.cs.toronto.edu/~hinton/absps/tr00-004.pdf -- "Training Products of Experts by Minimizing Contrastive Divergence"

http://www.eecg.toronto.edu/~pc/research/publications/ly.fpga2009.submitted.pdf -- "A High-Performance FPGA Architecture for Restricted
Boltzmann Machines" Ly & Chow 2009

https://pdfs.semanticscholar.org/85fa/f7c3c05388e2bcd097a416606bdd88fc0c7c.pdf -- "A MULTI-FPGA ARCHITECTURE FOR STOCHASTIC RESTRICTED BOLTZMANN MACHINES" Ly & Chow 2009

== Variational Renormalization ==

https://arxiv.org/pdf/1410.3831 -- "An exact mapping between the Variational Renormalization Group and Deep Learning"

== Neuromorphic Stuff ==

https://arxiv.org/pdf/1508.01008.pdf -- "INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks" Chung, Shin & Kang 2015

== Markov Chain Monte Carlo ==

http://www.cs.princeton.edu/courses/archive/spr06/cos598C/papers/AndrieuFreitasDoucetJordan2003.pdf
-- "An Introduction to MCMC for Machine Learning"

http://jmlr.org/proceedings/papers/v37/salimans15.pdf
-- "Markov Chain Monte Carlo and Variational Inference: Bridging the Gap"

https://www.umiacs.umd.edu/~resnik/pubs/LAMP-TR-153.pdf -- "Gibbs Sampling for the Uninitiated"

==Entrainment==

http://www.scirp.org/Journal/PaperDownload.aspx?paperID=33251 -- "Alpha Rhythms Response to 10 Hz Flicker Is Wavelength Dependent"

http://www.brainmachine.co.uk/wp-content/uploads/Herrmann_Flicker.pdf -- "EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena"

http://www.jneurosci.org/content/23/37/11621.full.pdf -- "Human Cerebral Activation during Steady-State Visual-Evoked Responses"

http://www.dauwels.com/Papers/CogDyn%202009.pdf -- "On the synchrony of steady state visual evoked potentials and oscillatory burst events"

https://www.tu-ilmenau.de/fileadmin/public/lorentz-force/publications/peer/2012/haueisen2012/Halbleib_JCN_2012_Topographic_analysis_photic_driving.pdf -- "Topographic Analysis of Engagement and Disengagement of Neural Oscillators in Photic Driving: A Combined Electroencephalogram/Magnetoencephalogram Study"

==Mining Scientific Literature==

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC153503/pdf/1471-2105-4-11.pdf -- "PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine" Donaldson 2003 BMC Bioinformatics

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674139/pdf/pcbi.1004630.pdf -- "Text Mining for Protein Docking" Badal 2015 PLoS Comput Biol.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4691339/pdf/bav116.pdf -- "Biocuration with insufficient resources and fixed timelines" Rodriguez-Esteban 2015 Database: The Journal of Biological Databases and Curation

==(not necessarilly very) Current Discussion==

re Tononi's "Integrated Information Theory" http://www.scottaaronson.com/blog/?p=1799

(19 February 2014) starting to think about possibility for experiments (loosely) related to [https://en.wikipedia.org/wiki/Visual_evoked_potential Visual Evoked Potential] research again - for instance:

http://www.biosemi.com/publications/pdf/Bierman.pdf -- Wave function collapse

[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999

[[File:CoherentEEGAmbiguousFigureBinding.pdf]] -- "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" -- Klemm, Li, and Hernandez 2000

Note these two papers flog coherence measures - not trying to focus so much on that analysis right now, more interested in general understanding of what these experiments are about with possible goal of designing simpler experiments & analysis of similar perceptual/cognitive phenomena.

Here is an article that looks more directly at visual evoked potential measures:

[[File:ERP_Stereoscopic.pdf]] -- "Evoked Responses to Distinct and Nebulous Stereoscopic Stimuli" -- Dunlop et al 1983

(11 September 2013) more on analysis methods:

http://slesinsky.org/brian/misc/eulers_identity.html

http://www.dspguide.com/ch8/1.htm

[[File:Fftw3.pdf]]

[[File:ParametricEEGAnalysis.pdf]]

[[File:ICATutorial.pdf]]

[[File:ICAFrequencyDomainEEG.pdf]]

(21 August 2013) - readings relating statistical (etc math / signal processing / pattern recognition / machine learning) methods for EEG data interpretation. A lot of stuff, a bit of nonsense ... and ... statistics!

Would be good to identify any papers suitable for more in-depth study. Currently have a wide field to graze for selections:

[[File:DWTandFFTforEEG.pdf]] "EEG Classifier using Fourier Transform and Wavelet Transform" -- Maan Shaker, 2007

[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999

[[File:KulaichevCoherence.pdf]] -- "The Informativeness of Coherence Analysis in EEG Studies" -- A. P. Kulaichev 2009 ''note: interesting critical perspective re limitations, discussion of alternative analytics''

[[File:ContinuousAndDiscreteWaveletTransforms.pdf]] -- review of (pre-1990) wavelet literature -- Christopher Heil and David Walnut, 1989

[[File:EEGGammaMeditation.pdf]] -- "Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self" -- Dietrich Lehman et al 2001

http://neuro.hut.fi/~pavan/home/Hyvarinen2010_FourierICA_Neuroimage.pdf - "Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis" -- Aapo Hyvarinen, Pavan Ramkumar, Lauri Parkkonen, Riitta Hari - paper published in Neuroimage vol 49 (2010)

----

[http://www.nickgillian.com/software/grt OpenSource Machine Learning Algs from NG @MIT]
 [https://www.usenix.org/system/files/conference/usenixsecurity12/sec12-final56.pdf Consumer grade EEG used to see "P300" reponse] and for thoes with a short attention span [http://www.extremetech.com/extreme/134682-hackers-backdoor-the-human-brain-successfully-extract-sensitive-data tldr]
 (discussed at meetup Wednesday 31 July 2013)
 "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" Klemm, Li, and Hernandez 2000
 [[File:CoherentEEGAmbiguousFigureBinding.pdf]]
 "We tested the hypothesis that perception of an alternative image in ambiguous figures would be manifest as high-frequency (gamma) components that become synchronized over multiple scalp sites as a "cognitive binding" process occurs."
 

----

[http://dreamsessions.net art, dream, and eeg]

----

[http://www.believermag.com/issues/200710/?read=article_aviv mind v brain, hobson v solms]
 http://en.wikipedia.org/wiki/Activation-synthesis_hypothesis
 [[File:HobsomREMDreamProtoconsciousness.pdf|Hobson09ProtosconsciousnessREMDream]]

"Hobson and McCarley originally proposed in the 1970s that the differences in the waking-NREM-REM sleep cycle was the result of interactions between aminergic REM-off cells and cholinergic REM-on cells.[4] This was perceived as the activation-synthesis model, stating that brain activation during REM sleep results in synthesis of dream creation.[1][1] Hobson's five cardinal characteristics include: intense emotions, illogical content, apparent sensory impressions, uncritical acceptance of dream events, and difficulty in being remembered."

----

Berkeley Labs

[http://gallantlab.org/index.html Gallant Group]
 [http://walkerlab.berkeley.edu/ Walker Group]
 [http://socrates.berkeley.edu/~plab/ Palmer Group]

==Sleep Research==

[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2335403/ Comment on the AASM Manual for the Scoring of Sleep and Associated Events]

==random tangents==
(following previous discussion) - we might select a few to study in more depth
(... or not! Plenty more to explore - suggestions (random or otherwise) are welcome.
http://www.meltingasphalt.com/neurons-gone-wild/ --
Neurons Gone Wild - Levels of agency in the brain.

'''stereoscopic perception:'''
*[[File:ERP_Stereoscopic.pdf]]

some (maybe) interesting background on Information Theory (cool title...)
Claude Shannon: "Communication in the Presence of Noise"
[[File:Shannon_noise.pdf]]
"We will call a system that transmits without errors at the rate ''C'' an ideal system.
Such a system cannot be achieved with any finite encoding process
but can be approximated as closely as desired."

wikipedia etc quick reads:
https://en.wikipedia.org/wiki/Eeg
https://en.wikipedia.org/wiki/Neural_synchronization
https://en.wikipedia.org/wiki/Event-related_potentials
http://www.scholarpedia.org/article/Spike-and-wave_oscillations
http://www.scholarpedia.org/article/Thalamocortical_oscillations

==Previously==

[http://www.psychiclab.net/ Masahiro's EEG Device/IBVA Software]

[http://www.instructables.com/id/open-brain-wave-interface-hardware-1/ and ... open source hardware design and kits on instructables.com]

[http://brainstorms.puzzlebox.info/ Puzzlebox - Opensource BCI Developers]

Morgan from GazzLab @ MissionBay/UCSF

https://github.com/gazzlab

Let's ease into a lightweight "journal club" discussion with this technical report from NeuroSky.

Name: A user-friendly SSVEP-based brain-computer interface using a time-domain classifier, Luo A and Sullivan TJ 2010

URL: [[File:NeuroSkyVEP.pdf‎]]

Please add your comments & questions here.

==Background Reading==

http://nanosouffle.net/ (view into Arxiv.org)

Name: Hunting for Meaning after Midnight, Miller 2007

URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0002.pdf>

Name: Broken mirrors, Ram, VS, & Oberman, LM, 2006, Nov

URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0003.pdf>

Ramachandran Critique

http://blogs.scientificamerican.com/guest-blog/2012/11/06/whats-so-special-about-mirror-neurons/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773693/

Sleep/Dream Studies

http://www.cns.atr.jp/dni/en/publications/

==NeuroSky Docs==
[[File:NeuroSkyDongleProtocol.pdf‎]]

[[File:NeuroSkyCommunicationsProtocol.pdf‎]]

==Android Neutral Network Fuzzy Learning app==
[https://play.google.com/store/apps/details?id=com.faadooengineers.free_neuralnetworkandfuzzysystems Android Neutral Network Fuzzy Learning app in Play Store]

==Learning about Neural Networks==
* What type of network? [http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma RMB (Restricted Boltzmann Machine) vs Autoencoder/MLP vs CNN (Convolutional Neural Networks)]
* Andrej Karpathy's [http://cs.stanford.edu/people/karpathy/convnetjs/ Convolutional Neural Network coded in JavaScript (ConvNetJS)]
* Andrej Karpathy's [http://karpathy.github.io/2015/10/25/selfie/ What a Deep Neural Network thinks about your #selfie (background on Convolutional Neural Networks for image recognition and classification)]
* [https://blog.webkid.io/neural-networks-in-javascript/ Neural Networks in JavaScript w/MNIST]
* [http://www.antoniodeluca.info/blog/10-08-2016/neural-networks-in-javascript.html Another NN in JS]
* [http://caza.la/synaptic/ The Synaptic "architecture-free" neural network library in JS]

DreamTeam/Reading

2018-03-01T06:50:18Z

192.195.80.12:

(note wiki contains some useful clues re previous neuro research at Noisebridge ... For example, the [[Analog_EEG_Amp]] page describes some project ideas and work done by others here in 2012)

https://noisebridge.net/wiki/NBDSM -- noiseBridge Deepnet and Statistical Mechanics -- first meetup @ noisebridge 7/6/17 at 7PM

https://metacademy.org/
-- machine learning knowledge graph

https://machinelearningguide.libsyn.com/rss -- machine learning guide podcast

http://www.thetalkingmachines.com/ -- podcast

https://karpathy.github.io/2015/05/21/rnn-effectiveness/

http://alexandre.barachant.org/papers/

http://ncs.ethz.ch/publications -- neuromorphic cognitive systems

https://github.com/crillab/gophersat/blob/master/examples/sat-for-noobs.md -- SAT solvers

https://media.ccc.de/v/34c3-8948-low_cost_non-invasive_biomedical_imaging -- Open EIT 34c3 talk https://github.com/OpenEIT

http://acrovirt.org/ -- sensors

http://www.neuroeducate.com/ -- citizen neuroscience

https://www.youtube.com/watch?v=9mZuyUzyN4Q -- "Categories for the Working Hacker"

http://radicalsciencenews.org/599-2/ -- "Deep Learning Fuels Nvidia’s Self-Driving Car Technology"

== OpenCV ==

http://arnab.org/blog/so-i-suck-24-automating-card-games-using-opencv-and-python

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.585&rep=rep1&type=pdf -- "Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm"

== Category Theory ==

https://arxiv.org/pdf/1711.10455 -- "Backprop as Functor: A compositional perspective on supervised learning"

== Proof Searcher ==

https://arxiv.org/pdf/cs/0207097 -- "Optimal Ordered Problem Solver"

http://people.idsia.ch/~juergen/ultimatecognition.pdf -- "Ultimate Cognition a la Gödel"

http://people.idsia.ch/~juergen/selfreflection.pdf -- "Towards an Actual Gödel Machine Implementation"

== Capsule Models ==

https://arxiv.org/pdf/1710.09829.pdf -- "Dynamic Routing Between Capsules"

https://openreview.net/pdf?id=HJWLfGWRb -- "Matrix Capsules with EM Routing"

== Infrared Neuroimaging ==

http://www.ecse.rpi.edu/~yazici/bio_book.pdf -- "Near Infrared Imaging and Spectroscopy for Brain Activity Monitoring"

== Geometry ==

http://arxiv.org/abs/1710.10784 -- "How deep learning works --The geometry of deep learning"

== Affective Computing ==

http://affect.media.mit.edu/pdfs/05.ahn-picard-acii.pdf -- "Affective-Cognitive Learning and Decision
Making: A Motivational Reward Framework For Affective Agents"

== Explainability ==

http://arxiv.org/abs/1708.01785 -- "Interpreting CNN knowledge via an Explanatory Graph"

== NLP ==

https://arxiv.org/pdf/1605.06640 -- "Programming with a Differentiable Forth Interpreter"

https://pdfs.semanticscholar.org/f683/dbe8a22d633ad3a2cff379b055b26684a838.pdf -- "Solving General Arithmetic Word Problems"

https://arxiv.org/pdf/1611.04558.pdf -- "Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation"

http://emnlp2014.org/papers/pdf/EMNLP2014162.pdf -- "GloVe: Global Vectors for Word Representation"

== RNNs ==

https://arxiv.org/pdf/1611.01576.pdf -- "Quasi Recurrent Neural Networks"

== Hyper-parameter Optimization ==

https://arxiv.org/abs/1603.06560 -- "Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization"

== Transfer Learning ==

http://arxiv.org/abs/1710.10776v1 -- "Transfer Learning to Learn with Multitask Neural Model Search"

== Reinforcement Learning ==

http://www2.hawaii.edu/~sstill/StillPrecup2011.pdf -- "An information-theoretic approach to curiosity-driven reinforcement learning"

https://arxiv.org/abs/1605.06676 -- "Learning to Communicate with Deep Multi-Agent Reinforcement Learning"

== Learning to Learn ==

https://arxiv.org/pdf/1703.01041.pdf -- "Large-Scale Evolution of Image Classifiers"

https://arxiv.org/pdf/1611.01578 -- "Neural Architecture Search with Reinforcement Learning"

== The Utility of "Noise" in ML ==

https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf -- "Dropout: A Simple Way to Prevent Neural Networks from Overfitting"

http://yann.lecun.com/exdb/publis/pdf/lecun-90b.pdf -- "Optimal Brain Damage"

https://arxiv.org/pdf/1502.01852.pdf -- "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"

== One-shot learning ==

https://arxiv.org/abs/1605%2E06065 -- "One-shot Learning with Memory-Augmented Neural Networks"

== Program Synthesis ==

https://pdfs.semanticscholar.org/0163/35ce7e0a073623e1deac7138b28913dbf594.pdf -- "Human-level concept learning through probabilistic program induction"

https://arxiv.org/pdf/1511.06279.pdf -- "Neural Programmer: Inducing Latent Programs with Gradient Descent"

https://arxiv.org/abs/1608.04428 -- "TerpreT: A Probabilistic Programming Language for Program Induction" Gaunt et al 2016

== Machine Learning Interaction ==

https://teachablemachine.withgoogle.com/#

== Game Theory ==

https://arxiv.org/abs/1707.01068v1 - Maintaining cooperation in complex social dilemmas using deep reinforcement learning

== Questions of Physics and Free Will ==

http://www.scottaaronson.com/papers/giqtm3.pdf - The Ghost in the Quantum Turing Machine

== CNN ==

https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/ - "A Beginner's Guide To Understanding Convolutional Neural Networks"

https://adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/ - "A Beginner's Guide To Understanding Convolutional Neural Networks Part 2"

http://scs.ryerson.ca/~aharley/vis/harley_vis_isvc15.pdf -- "An Interactive Node-Link Visualization
of Convolutional Neural Networks"

http://white.stanford.edu/teach/index.php/An_Introduction_to_Convolutional_Neural_Networks

http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf -- "Learning to Generate Chairs With Convolutional Neural Networks"

http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf
-- "What's Wrong With Deep Learning?"

== Mind-Body Relations ==

http://www.pnas.org/content/111/20/7379.full.pdf -- "Voluntary activation of the sympathetic nervous system and attenuation of the innate immune response in humans"

== Math ==

https://arxiv.org/pdf/1311.1090.pdf -- "Polyhedrons and Perceptrons Are Functionally Equivalent"

Example code and training data using polyhedrons developed by author of above paper: https://www.noisebridge.net/wiki/DreamTeam#Code

== Bayesian Inference ==

https://noisebridge.net/images/e/ef/Perception_is_in_the_Details12.pdf --
"Perception is in the Details: A Predictive Coding Account of the Psychedelic Phenomenon"

http://rsif.royalsocietypublishing.org/content/10/86/20130475 --
"Life as we know it"

http://jmlr.csail.mit.edu/proceedings/papers/v31/wang13b.pdf --
"Collapsed Variational Bayesian Inference for Hidden Markov Models"

http://www.datalab.uci.edu/papers/nips06_cvb.pdf --
"A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation"

http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf --
"Hierarchical Bayesian inference in the visual cortex"

https://www.researchgate.net/profile/Til_Bergmann/publication/262423308_Temporal_coding_organized_by_coupled_alpha_and_gamma_oscillations_prioritize_visual_processing/links/0deec537d1bfda474c000000/Temporal-coding-organized-by-coupled-alpha-and-gamma-oscillations-prioritize-visual-processing.pdf --
"Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing"

http://www.cell.com/neuron/pdf/S0896-6273(15)00823-5.pdf --
"Rhythms for Cognition: Communication through Coherence"

http://www.biorxiv.org/content/biorxiv/early/2014/05/06/004804.full.pdf --
"Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels"

== Speech Recognition ==

https://arxiv.org/pdf/1612.00694v1 -- "ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA"

== Sound Classification ==

https://arxiv.org/pdf/1608.04363v2 -- "Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification"

https://arxiv.org/pdf/1605.09507 "Deep convolutional neural networks for predominant instrument recognition in polyphonic music"

== Hardware Implementations - FPGA, GPU, etc ==

https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf --
"Artificial neural networks in hardware: A survey of two decades of progress"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.133.9185&rep=rep1&type=pdf
"A Self-Repairing Multiplexer-Based FPGA Inspired by Biological Processes"

http://www.genetic-programming.com/jkpdf/fpga1998.pdf -- "Evolving Computer Programs using Rapidly Reconfigurable Field-Programmable Gate Arrays and Genetic Programming"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.2588&rep=rep1&type=pdf -- "Flexible Implementation of Genetic Algorithms on FPGAs"

http://www.users.muohio.edu/jamiespa/html_papers/gem_10.pdf -- "Revisiting Genetic Algorithms for the FPGA Placement Problem"

https://arxiv.org/pdf/1609.09296v1 -- "Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.7533&rep=rep1&type=pdf -- "FPGA-TARGETED NEURAL ARCHITECTURE FOR EMBEDDED ALERTNESS DETECTION"

https://arxiv.org/pdf/1611.02450v1 -- "PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks"

https://arxiv.org/pdf/1605.06402v1 -- "Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks"

https://homes.cs.washington.edu/~luisceze/publications/snnap-hpca-2015.pdf -- "SNNAP: Approximate Computing on Programmable SoCs via Neural Acceleration"

https://arxiv.org/pdf/1701.00485v2 -- "Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices"

== VLSI ==

http://ncs.ethz.ch/pubs/pdf/Indiveri_etal06.pdf -- "A VLSI array of low-power spiking neurons and bistable synapses with spike–timing dependent plasticity"

== Pruning ==

http://papers.nips.cc/paper/5784-learning-both-weights-and-connections-for-efficient-neural-network.pdf --
"Learning both Weights and Connections for Efficient Neural Networks"

https://arxiv.org/pdf/1701.04465 -- "The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning"

https://arxiv.org/pdf/1512.08571 -- "Structured Pruning of Deep Convolutional Neural Networks"

https://arxiv.org/pdf/1611.01427 -- "Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks"

== Efficient Neural Networks via Compression, Quantization, Model Reduction, etc ==

https://arxiv.org/pdf/1504.04788 -- "Compressing Neural Networks with the Hashing Trick"

https://arxiv.org/pdf/1509.08745 -- "Compression of Deep Neural Networks on the Fly"

https://arxiv.org/pdf/1502.03436 -- "An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections"

https://arxiv.org/pdf/1510.00149 -- "Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding"

https://arxiv.org/pdf/1612.00891 -- "Parameter Compression of Recurrent Neural Networks and Degradation of Short-term Memory"

https://arxiv.org/pdf/1609.07061 -- "Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations"

https://arxiv.org/pdf/1607.05418 -- "Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off"

https://arxiv.org/pdf/1602.08194 -- "Scalable and Sustainable Deep Learning via Randomized Hashing"

https://arxiv.org/pdf/1508.05463 -- "StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity"

https://arxiv.org/pdf/1412.7024 -- "Training Deep Neural Networks with Low Precision Multiplications"

https://arxiv.org/pdf/1612.03940 -- "Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks"

https://arxiv.org/pdf/1609.00222 -- "Ternary Neural Networks for Resource-Efficient AI Applications"

== Neural Network Hyperparameter Optimization ==

https://arxiv.org/pdf/1601.00917 -- "DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks"

== Neural Network based EEG Analysis ==
end
http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"

http://inter-eng.upm.ro/2012/files/proceedings/papers/paper72.pdf --
"Neural Network Parallelization on FPGA Platform for EEG Signal Classification"

== Seizure Detection ==

also see https://noisebridge.net/wiki/Kaggle for a (September 2016) current project!

and https://github.com/kevinjos/kaggle-aes-seizure-prediction (some earlier exploration, November 2014)

(broken link, sorry) http://www.sersc.org/journals/ijsip/vol7_no5/26.pdf --
"A Neural Network Model for Predicting Epileptic Seizures based on Fourier-Bessel Functions"

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf --
"A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"

(another broken link) http://cs.uni-muenster.de/Professoren/Lippe/diplomarbeiten/html/eisenbach/Untersuchte%20Artikel/PPHD+00.pdf --
"Recurrent neural network based preenddiction of epileptic seizures in intra- and extracranial EEG"

== Visible Light Sensor Network ==

http://infoteh.etf.unssa.rs.ba/zbornik/2016/radovi/KST-1/KST-1-15.pdf --
"Analysis of Visible Light Communication System for Implementation in Sensor Networks"

== Neurophysiology ==

http://www.buzsakilab.com/content/PDFs/BuzsakiKoch2012.pdf -- "The origin of extracellular fields and
currents — EEG, ECoG, LFP and spikes"

== Signal Processing ==

http://provideyourown.com/2011/analogwrite-convert-pwm-to-voltage/ -- "Arduino’s AnalogWrite – Converting PWM to a Voltage"

http://sim.okawa-denshi.jp/en/PWMtool.php -- "RC Low-pass Filter Design for PWM (Transient Analysis Calculator)"

== Hyperdimensional Computing ==

http://redwood.berkeley.edu/pkanerva/papers/kanerva09-hyperdimensional.pdf --
"Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors"

http://arxiv.org/pdf/1602.03032.pdf --
"Associative Long Short-Term Memory"

== Bird Flocks and Maximum Entropy ==

http://arxiv.org/pdf/1107.0604v1 --
"Statistical Mechanics and Flocks of Birds"

http://arxiv.org/pdf/1307.5563v1 --
"Social interactions dominate speed control in driving natural flocks toward criticality"

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_penfield.pdf --
"Information and Entropy (Course Notes)"

== Whale Songs ==

https://arxiv.org/pdf/1307.0589.pdf -- "The Orchive : Data mining a massive bioacoustic archive"

https://www.researchgate.net/profile/Herbert_Roitblat/publication/13429327_The_neural_network_classification_of_false_killer_whale_%28Pseudorca_crassidens%29_vocalizations/links/540d2ff60cf2df04e75478cd.pdf -- "The neural network classification of false killer whale (Pseudorca crassidens) vocalizations"

http://users.iit.demokritos.gr/~paliourg/papers/PhD.pdf -- "REFINEMENT OF TEMPORAL CONSTRAINTS IN AN EVENT RECOGNITION SYSTEM USING SMALL DATASETS"

https://www.nersc.no/sites/www.nersc.no/files/master_thesis_sebastian_menze.pdf -- "Estimating fin whale distribution from ambient noise spectra using Bayesian inversion"

http://sis.univ-tln.fr/~glotin/IJCNN2015_IHMMbioac_BartChamGlot.pdf -- "Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis"

https://www.inf.ed.ac.uk/publications/thesis/online/IM030057.pdf -- "Hidden Markov Model Clustering of Acoustic Data"

http://danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/ -- Using deep learning to listen for whales

== Computational Cognitive Neuroscience ==

http://www.pnas.org/content/110/41/16390.full -- "Indirection and symbol-like processing in the prefrontal cortex and basal ganglia"

http://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/docs/jaf.pdf -- "Connectionism and Cognitive Architecture: A Critical Analysis"

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2728678/pdf/nihms131814.pdf -- Neves et al 2008 "Cell Shape and Negative Links in Regulatory Motifs Together Control Spatial Information Flow in Signaling Networks"

http://psych.colorado.edu/~oreilly/papers/AisaMingusOReilly08.pdf -- "The Emergent Neural Modeling System"

https://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators

== Text Generation ==

http://www.cs.toronto.edu/~ilya/pubs/2011/LANG-RNN.pdf -- "Generating Text with Recurrent Neural Networks"

== Games ==

http://setgame.com/sites/default/files/teacherscorner/COGNITIVE%20MODELING%20WITH%20SET.pdf -- "How to Construct a Believable Opponent using Cognitive Modeling in the Game of Set"

http://www-personal.umich.edu/~charchan/SET.pdf -- "SETs and Anti-SETs: The Math Behind the Game of SET"

http://personal.plattsburgh.edu/quenelgt/talks/set.pdf -- "Introduction to Set"

http://web.engr.illinois.edu/~pbg/papers/set.pdf -- "On the Complexity of the Game of Set"

http://www.warwick.ac.uk/staff/D.Maclagan/papers/set.pdf -- "The Card Game Set"

http://www.math.ucdavis.edu/~anne/FQ2011/set_game.pdf -- "The Game Set"

https://www.youtube.com/watch?v=k2rgzZ2WXKo -- "Best Practices for Procedural Narrative Generation" Chris Martens

== Large Scale Brain Simulation ==

http://www.nowere.net/b/arch/96550/src/1378907656268.pdf -- "A world survey of artificial brain projects, Part I: Large-scale brain simulations"

== Music ==

http://cmr.soc.plymouth.ac.uk/publications/bci-wkshop.pdf -- "ON GENERATING EEG FOR CONTROLLING MUSICAL SYSTEMS"

== Code ==

https://github.com/nbdt/gotrain (our ANN code)

https://github.com/nbdt/openbci-golang-server (EEG data acquisition and visualization software for OpenBCI)

== Hidden Markov Models ==

http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf
-- "A Revealing Introduction to Hidden Markov Models"

http://www.jelmerborst.nl/pubs/Borst2013b.pdf
-- "Discovering Processing Stages by combining EEG with Hidden Markov Models"

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf
-- "A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"

http://www.schalklab.org/sites/default/files/misc/Coupled%20hidden%20markov%20model%20for%20electrocorticographic%20signal%20classification.pdf
-- "Coupled Hidden Markov Model for Electrocorticographic Signal Classification"

== Long Short Term Memory ==

http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
-- "Long Short-Term Memory"

ftp://ftp.idsia.ch/pub/juergen/2002_icannMusic.pdf
-- "Learning The Long-Term Structure of the Blues"

http://www.overcomplete.net/papers/nn2012.pdf
-- "A generalized LSTM-like training algorithm for second-order recurrent neural networks"

http://www.iro.umontreal.ca/~eckdoug/papers/2003_nn.pdf
-- "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets"

http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html
-- "Long Short-Term Memory dramatically improves Google Voice etc"

https://arxiv.org/pdf/1511.05552v4.pdf --
"Recurrent Neural Networks Hardware Implementation on FPGA"

http://vast.cs.ucla.edu/sites/default/files/publications/ASP-DAC2017-1352-11.pdf --
"FPGA-based Accelerator for Long Short-Term Memory Recurrent Neural Networks"

== Question Answering ==

http://www.overcomplete.net/papers/bica2012.pdf
-- "Neural Architectures for Learning to Answer Questions"

https://cs.umd.edu/~miyyer/pubs/2014_qb_rnn.pdf
-- "A Neural Network for Factoid Question Answering over Paragraphs"

http://arxiv.org/pdf/1502.05698.pdf
-- "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"

http://www.peterschueller.com/pub/2014/2014_winograd_schemas_relevance_knowledge_graphs.pdf
-- "Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs"

http://ijcai.org/papers15/Papers/IJCAI15-190.pdf
-- "Towards Addressing the Winograd Schema Challenge — Building and Using a Semantic Parser and a Knowledge Hunting Module"

http://arxiv.org/pdf/1506.05869v2.pdf
-- "A Neural Conversational Model"

http://arxiv.org/pdf/1508.05508v1.pdf
-- "Towards Neural Network-based Reasoning"

http://www.visualqa.org/vqa_iccv2015.pdf
-- "VQA: Visual Question Answering"

== Propagators ==
Cells must support three operations:
*add some content
*collect the content currently accumulated
*register a propagator to be notified when the accumulated content changes
*When new content is added to a cell, the cell must merge the addition with the content already present. When a propagator asks for the content of a cell, the cell must deliver a complete summary of the information that has been added to it.
*The merging of content must be commutative, associative, and idempotent. The behavior of propagators must be monotonic with respect to the lattice induced by the merge operation.
*http://web.mit.edu/~axch/www/art.pdf *http://groups.csail.mit.edu/mac/users/gjs/propagators/
*http://dustycloud.org/blog/sussman-on-ai/

== Boosting ==

http://www.cs.princeton.edu/courses/archive/spr08/cos424/readings/Schapire2003.pdf
-- "The Boosting Approach to Machine Learning An Overview"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.9955&rep=rep1&type=pdf
-- "Ensembling Neural Networks: Many Could Be Better Than All"

http://www.cs.columbia.edu/~rocco/Public/mlj9.pdf
-- "Random Classification Noise Defeats All Convex Potential Boosters"

== Support Vector Machines ==

http://research.microsoft.com/en-us/um/people/cburges/papers/svmtutorial.pdf
-- "A Tutorial on Support Vector Machines for Pattern Recognition"

== Wire Length / Small World Networks ==

http://koulakovlab.cshl.edu/publications/koulakov_chklovskii_2000.pdf
-- "A wire length minimization approach to ocular dominance patterns in mammalian visual cortex"

http://papers.nips.cc/paper/1910-foundations-for-a-circuit-complexity-theory-of-sensory-processing.pdf
-- "Foundations for a Circuit Complexity Theory of Sensory Processing"

https://www.nada.kth.se/~cjo/documents/small_world.pdf
-- "Small-World Connectivity and Attractor Neural Networks"

http://www.doc.ic.ac.uk/~mpsha/ShanahanPhysRevEPreprint.pdf
-- "The Dynamical Complexity of Small-World Networks of Spiking Neurons"

http://www.dam.brown.edu/people/elie/papers/small_world.pdf
-- "Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.4501&rep=rep1&type=pdf
-- "Transition from Random to Small-World Neural Networks by STDP Learning Rule"

http://tamar.tau.ac.il/~eshel/papers/J_Neural_Engineering.pdf
-- "Compact self-wiring in cultured neural networks"

== Backpropagation ==

http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf -- "Neural Networks - A Systematic Introduction"

http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf (works through simple example)

http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/

http://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf

also see http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf (entire book "Neural Networks - a Systemic Introduction" by Raul Rojas)

http://work.caltech.edu/lectures.html Hoeffding's inequality, VC Dimension and Back Propagation ANN

http://www.cs.stir.ac.uk/research/publications/techreps/pdf/TR148.pdf ("Learning XOR: exploring the space of a classic problem")

http://www.sysc.pdx.edu/classes/Werbos-Backpropagation%20through%20time.pdf
-- "Backpropagation Through Time: What it Does and How to Do It"

== Computer Vision ==

http://www.cv-foundation.org/openaccess/CVPR2015.py -- some interesting papers here

http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf -- "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"

== Visual Perception (Biological Systems) ==

http://cbcl.mit.edu/publications/ps/Serre_etal_PBR07.pdf
-- "A quantitative theory of immediate visual recognition"

http://www.dam.brown.edu/ptg/REPORTS/Invariance.pdf
-- "Invariance and Selectivity in the Ventral Visual Pathway"

http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf
-- "Hierarchical Bayesian inference in the visual cortex"

== Neural Synchrony ==

http://arxiv.org/pdf/1312.6115.pdf
-- "Neuronal Synchrony in Complex-Valued Deep Networks"

== Spiking Neural Networks ==

http://ncs.ethz.ch/projects/evospike/publications/ICONIP2011%20Springer%20LNCS%20Nutta.pdf -- "EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network"

http://dai.fmph.uniba.sk/~benus/confers/CNGM_ICANN05.pdf -- "Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques"

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3902 -- "Pattern Recognition in a Bucket"

http://www.igi.tugraz.at/maass/psfiles/221.pdf -- "Noise as a Resource for Computation and Learning in Spiking Neural Networks"

http://web.stanford.edu/group/brainsinsilicon/goals.html -- Neurogrid

http://apt.cs.manchester.ac.uk/projects/SpiNNaker/ -- SpiNNaker

http://personalpages.manchester.ac.uk/staff/javier.navaridas/pubs/2012.jpdc.pdf -- more on SpiNNaker

http://apt.cs.manchester.ac.uk/ftp/pub/apt/papers/JavN_ICS09.pdf -- "Understanding the Interconnection Network of SpiNNaker"

==Hierarchical Temporal Memory==

https://github.com/numenta/nupic/wiki/Hierarchical-Temporal-Memory-Theory

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000532 -- "Towards a Mathematical Theory of Cortical Micro-circuits"

==Distributed Neural Networks==

https://www.paypal-engineering.com/2015/01/12/deep-learning-on-hadoop-2-0-2/ -- paypal engineering blog, paper describes implementation of neural network as described by [http://www.maths.bris.ac.uk/~maxvd/reduce_dim.pdf Hinton 2006] on Hadoop

http://www.sciencemag.org/content/suppl/2006/08/04/313.5786.504.DC1/Hinton.SOM.pdf -- supplementary material for [http://www.maths.bris.ac.uk/~maxvd/reduce_dim.pdf Hinton 2006]

http://www.cs.otago.ac.nz/staffpriv/hzy/papers/pdcs03.pdf -- "Parallelization of a Backpropagation Neural Network on a Cluster Computer"

http://arxiv.org/pdf/1404.5997v2.pdf -- "One weird trick for parallelizing convolutional neural networks"

http://www.ics.uci.edu/~jmoorkan/pub/gpusnn-ijcnn.pdf -- "Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors"

==Mixture of Experts==

http://www.cs.toronto.edu/~fritz/absps/jjnh91.pdf -- "Adaptive Mixtures of Local Experts"

==Hopfield nets and RBMs==

http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf -- Yoshua Bengio, 2009, Learning Deep Architectures for AI

http://deeplearning.cs.cmu.edu/ -- Syllabus for cs course on deep learning, possible source of literature for the library

https://class.coursera.org/neuralnets-2012-001/lecture -- Lecture 11 - 15 to aid in understanding of Hinton 2006 paper above

http://www.pnas.org/content/79/8/2554.full.pdf -- "Neural networks and physical systems with emergent collective computational abilities" (Hopfield 1982)

http://page.mi.fu-berlin.de/rojas/neural/chapter/K13.pdf -- "The Hopfield Model" (Rojas 1996)

http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"

http://www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/cameraready_xwjiabibe.pdf -- "A Novel Semi-supervised Deep Learning Framework
for Affective State Recognition on EEG Signals"

http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf -- "A Practical Guide to Training Restricted Boltzmann Machines"

http://arxiv.org/pdf/1503.07793v2.pdf
-- "Gibbs Sampling with Low-Power Spiking Digital Neurons"

http://arxiv.org/pdf/1311.0190v1 -- "On the typical properties of inverse problems in statistical mechanics" Iacopo Mastromatteo 2013

http://jmlr.org/proceedings/papers/v5/salakhutdinov09a/salakhutdinov09a.pdf -- "Deep Boltzmann Machines" Salakhutdinov & Hinton 2009

http://www.cs.toronto.edu/~hinton/absps/tr00-004.pdf -- "Training Products of Experts by Minimizing Contrastive Divergence"

http://www.eecg.toronto.edu/~pc/research/publications/ly.fpga2009.submitted.pdf -- "A High-Performance FPGA Architecture for Restricted
Boltzmann Machines" Ly & Chow 2009

https://pdfs.semanticscholar.org/85fa/f7c3c05388e2bcd097a416606bdd88fc0c7c.pdf -- "A MULTI-FPGA ARCHITECTURE FOR STOCHASTIC RESTRICTED BOLTZMANN MACHINES" Ly & Chow 2009

== Variational Renormalization ==

https://arxiv.org/pdf/1410.3831 -- "An exact mapping between the Variational Renormalization Group and Deep Learning"

== Neuromorphic Stuff ==

https://arxiv.org/pdf/1508.01008.pdf -- "INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks" Chung, Shin & Kang 2015

== Markov Chain Monte Carlo ==

http://www.cs.princeton.edu/courses/archive/spr06/cos598C/papers/AndrieuFreitasDoucetJordan2003.pdf
-- "An Introduction to MCMC for Machine Learning"

http://jmlr.org/proceedings/papers/v37/salimans15.pdf
-- "Markov Chain Monte Carlo and Variational Inference: Bridging the Gap"

https://www.umiacs.umd.edu/~resnik/pubs/LAMP-TR-153.pdf -- "Gibbs Sampling for the Uninitiated"

==Entrainment==

http://www.scirp.org/Journal/PaperDownload.aspx?paperID=33251 -- "Alpha Rhythms Response to 10 Hz Flicker Is Wavelength Dependent"

http://www.brainmachine.co.uk/wp-content/uploads/Herrmann_Flicker.pdf -- "EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena"

http://www.jneurosci.org/content/23/37/11621.full.pdf -- "Human Cerebral Activation during Steady-State Visual-Evoked Responses"

http://www.dauwels.com/Papers/CogDyn%202009.pdf -- "On the synchrony of steady state visual evoked potentials and oscillatory burst events"

https://www.tu-ilmenau.de/fileadmin/public/lorentz-force/publications/peer/2012/haueisen2012/Halbleib_JCN_2012_Topographic_analysis_photic_driving.pdf -- "Topographic Analysis of Engagement and Disengagement of Neural Oscillators in Photic Driving: A Combined Electroencephalogram/Magnetoencephalogram Study"

==Mining Scientific Literature==

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC153503/pdf/1471-2105-4-11.pdf -- "PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine" Donaldson 2003 BMC Bioinformatics

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674139/pdf/pcbi.1004630.pdf -- "Text Mining for Protein Docking" Badal 2015 PLoS Comput Biol.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4691339/pdf/bav116.pdf -- "Biocuration with insufficient resources and fixed timelines" Rodriguez-Esteban 2015 Database: The Journal of Biological Databases and Curation

==(not necessarilly very) Current Discussion==

re Tononi's "Integrated Information Theory" http://www.scottaaronson.com/blog/?p=1799

(19 February 2014) starting to think about possibility for experiments (loosely) related to [https://en.wikipedia.org/wiki/Visual_evoked_potential Visual Evoked Potential] research again - for instance:

http://www.biosemi.com/publications/pdf/Bierman.pdf -- Wave function collapse

[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999

[[File:CoherentEEGAmbiguousFigureBinding.pdf]] -- "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" -- Klemm, Li, and Hernandez 2000

Note these two papers flog coherence measures - not trying to focus so much on that analysis right now, more interested in general understanding of what these experiments are about with possible goal of designing simpler experiments & analysis of similar perceptual/cognitive phenomena.

Here is an article that looks more directly at visual evoked potential measures:

[[File:ERP_Stereoscopic.pdf]] -- "Evoked Responses to Distinct and Nebulous Stereoscopic Stimuli" -- Dunlop et al 1983

(11 September 2013) more on analysis methods:

http://slesinsky.org/brian/misc/eulers_identity.html

http://www.dspguide.com/ch8/1.htm

[[File:Fftw3.pdf]]

[[File:ParametricEEGAnalysis.pdf]]

[[File:ICATutorial.pdf]]

[[File:ICAFrequencyDomainEEG.pdf]]

(21 August 2013) - readings relating statistical (etc math / signal processing / pattern recognition / machine learning) methods for EEG data interpretation. A lot of stuff, a bit of nonsense ... and ... statistics!

Would be good to identify any papers suitable for more in-depth study. Currently have a wide field to graze for selections:

[[File:DWTandFFTforEEG.pdf]] "EEG Classifier using Fourier Transform and Wavelet Transform" -- Maan Shaker, 2007

[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999

[[File:KulaichevCoherence.pdf]] -- "The Informativeness of Coherence Analysis in EEG Studies" -- A. P. Kulaichev 2009 ''note: interesting critical perspective re limitations, discussion of alternative analytics''

[[File:ContinuousAndDiscreteWaveletTransforms.pdf]] -- review of (pre-1990) wavelet literature -- Christopher Heil and David Walnut, 1989

[[File:EEGGammaMeditation.pdf]] -- "Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self" -- Dietrich Lehman et al 2001

http://neuro.hut.fi/~pavan/home/Hyvarinen2010_FourierICA_Neuroimage.pdf - "Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis" -- Aapo Hyvarinen, Pavan Ramkumar, Lauri Parkkonen, Riitta Hari - paper published in Neuroimage vol 49 (2010)

----

[http://www.nickgillian.com/software/grt OpenSource Machine Learning Algs from NG @MIT]
 [https://www.usenix.org/system/files/conference/usenixsecurity12/sec12-final56.pdf Consumer grade EEG used to see "P300" reponse] and for thoes with a short attention span [http://www.extremetech.com/extreme/134682-hackers-backdoor-the-human-brain-successfully-extract-sensitive-data tldr]
 (discussed at meetup Wednesday 31 July 2013)
 "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" Klemm, Li, and Hernandez 2000
 [[File:CoherentEEGAmbiguousFigureBinding.pdf]]
 "We tested the hypothesis that perception of an alternative image in ambiguous figures would be manifest as high-frequency (gamma) components that become synchronized over multiple scalp sites as a "cognitive binding" process occurs."
 

----

[http://dreamsessions.net art, dream, and eeg]

----

[http://www.believermag.com/issues/200710/?read=article_aviv mind v brain, hobson v solms]
 http://en.wikipedia.org/wiki/Activation-synthesis_hypothesis
 [[File:HobsomREMDreamProtoconsciousness.pdf|Hobson09ProtosconsciousnessREMDream]]

"Hobson and McCarley originally proposed in the 1970s that the differences in the waking-NREM-REM sleep cycle was the result of interactions between aminergic REM-off cells and cholinergic REM-on cells.[4] This was perceived as the activation-synthesis model, stating that brain activation during REM sleep results in synthesis of dream creation.[1][1] Hobson's five cardinal characteristics include: intense emotions, illogical content, apparent sensory impressions, uncritical acceptance of dream events, and difficulty in being remembered."

----

Berkeley Labs

[http://gallantlab.org/index.html Gallant Group]
 [http://walkerlab.berkeley.edu/ Walker Group]
 [http://socrates.berkeley.edu/~plab/ Palmer Group]

==Sleep Research==

[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2335403/ Comment on the AASM Manual for the Scoring of Sleep and Associated Events]

==random tangents==
(following previous discussion) - we might select a few to study in more depth
(... or not! Plenty more to explore - suggestions (random or otherwise) are welcome.
http://www.meltingasphalt.com/neurons-gone-wild/ --
Neurons Gone Wild - Levels of agency in the brain.

'''stereoscopic perception:'''
*[[File:ERP_Stereoscopic.pdf]]

some (maybe) interesting background on Information Theory (cool title...)
Claude Shannon: "Communication in the Presence of Noise"
[[File:Shannon_noise.pdf]]
"We will call a system that transmits without errors at the rate ''C'' an ideal system.
Such a system cannot be achieved with any finite encoding process
but can be approximated as closely as desired."

wikipedia etc quick reads:
https://en.wikipedia.org/wiki/Eeg
https://en.wikipedia.org/wiki/Neural_synchronization
https://en.wikipedia.org/wiki/Event-related_potentials
http://www.scholarpedia.org/article/Spike-and-wave_oscillations
http://www.scholarpedia.org/article/Thalamocortical_oscillations

==Previously==

[http://www.psychiclab.net/ Masahiro's EEG Device/IBVA Software]

[http://www.instructables.com/id/open-brain-wave-interface-hardware-1/ and ... open source hardware design and kits on instructables.com]

[http://brainstorms.puzzlebox.info/ Puzzlebox - Opensource BCI Developers]

Morgan from GazzLab @ MissionBay/UCSF

https://github.com/gazzlab

Let's ease into a lightweight "journal club" discussion with this technical report from NeuroSky.

Name: A user-friendly SSVEP-based brain-computer interface using a time-domain classifier, Luo A and Sullivan TJ 2010

URL: [[File:NeuroSkyVEP.pdf‎]]

Please add your comments & questions here.

==Background Reading==

http://nanosouffle.net/ (view into Arxiv.org)

Name: Hunting for Meaning after Midnight, Miller 2007

URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0002.pdf>

Name: Broken mirrors, Ram, VS, & Oberman, LM, 2006, Nov

URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0003.pdf>

Ramachandran Critique

http://blogs.scientificamerican.com/guest-blog/2012/11/06/whats-so-special-about-mirror-neurons/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773693/

Sleep/Dream Studies

http://www.cns.atr.jp/dni/en/publications/

==NeuroSky Docs==
[[File:NeuroSkyDongleProtocol.pdf‎]]

[[File:NeuroSkyCommunicationsProtocol.pdf‎]]

==Android Neutral Network Fuzzy Learning app==
[https://play.google.com/store/apps/details?id=com.faadooengineers.free_neuralnetworkandfuzzysystems Android Neutral Network Fuzzy Learning app in Play Store]

==Learning about Neural Networks==
* What type of network? [http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma RMB (Restricted Boltzmann Machine) vs Autoencoder/MLP vs CNN (Convolutional Neural Networks)]
* Andrej Karpathy's [http://cs.stanford.edu/people/karpathy/convnetjs/ Convolutional Neural Network coded in JavaScript (ConvNetJS)]
* Andrej Karpathy's [http://karpathy.github.io/2015/10/25/selfie/ What a Deep Neural Network thinks about your #selfie (background on Convolutional Neural Networks for image recognition and classification)]
* [https://blog.webkid.io/neural-networks-in-javascript/ Neural Networks in JavaScript w/MNIST]
* [http://www.antoniodeluca.info/blog/10-08-2016/neural-networks-in-javascript.html Another NN in JS]
* [http://caza.la/synaptic/ The Synaptic "architecture-free" neural network library in JS]