Noisebridge - User contributions [en]

Conflict Resolution

2017-07-22T02:41:11Z

198.27.140.180: /* Mindfulness towards Escalation: */

==Suggestions for if you are having problematic interactions within the Noisebridge community==

===Personal Confrontation:===
Is someone bothering you? Talk to them about it -- and be excellent while doing so. 
This is a pretty important step, and it usually has the desired effect. It should not be skipped if at all possible.

===Get Support:===
Did that not work? Or are you afraid to approach the other person? Ask someone else around the space who you like and/or trust. Maybe they can come along with you to talk to them, or talk to them as your proxy.

===Mediation:===
Did that not work? We have a [[Mediation]] page, where people can sign up to act as mediators. You should ask one of the people on it to help you mediate your conflict. They can actively mediate a discussion between you and the person with whom you are having conflict, or, if you prefer, the mediator can talk to that individual as your proxy.

===Mindfulness towards Escalation:===
If it seems appropriate, after talking with the original parties, the mediator (and indeed everyone involved) should start to tactfully ask around and find out if this is an isolated conflict or a more generalized problem in the community. Most personal problems at Noisebridge can be resolved through a series of calm one-on-one talks, and almost all of the rest can be solved by a series of mediated discussions. If mediation is unsuccessful, or if what is going on appears to be part of a larger pattern, the mediator may suggest that you bring your problem to a Safe Space Working Group for discussion. See [[Deescalation|here]] for more info on deescalation.

===Advocate:===
Discussing personal conflicts at the larger group level is not really considered all that excellent. On the other hand, a small supportive group environment more specifically committed to calm discussion and de-escalation '''can''' help defuse a problematic situation. If the parties involved cannot reach a resolution by talking with each other, or with the help of a [[Mediation|mediator]], the mediator can suggest calling a meeting of the Safe Space Working Group to involve other people to help resolve the conflict. If you try to follow these suggestions, that would be totally excellent.

Before a problem with an individual is brought to the level of calling a meeting of the Safe Space Working Group, someone must step forward to act as an advocate for the individual, even if that individual happens to be widely disliked. It is all too easy for conflict to make people act in ways that they later regret. There are sufficient people around the Space who are willing to act as advocates at the group level (see list of mediator volunteers on the [[Mediation]] wiki page).

===Reporting Misbehavior===
If you wish to report harassment anonymously or privately, you can send a message to secretary@noisebridge.net (or contact one of the people who have volunteered as a mediator on the [[Mediation|Mediation]] page).

{{ManualPage}}

DreamTeam/Reading

2017-07-07T05:11:21Z

198.27.140.180:

(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

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

== 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"

== 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"

== 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

==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

== 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

2017-04-08T22:44:50Z

198.27.140.180: /* Whale Songs */

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

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

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

== 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://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"

== 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"

== 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"

== Convolutional Neural Networks ==

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?"

== 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

==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"

==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

== 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]

Talk:DreamTeam/Brainduino

2017-03-22T03:09:33Z

198.27.140.180: /* March 21 2017 */

==March 21 2017==
Q&A with Masahiro!

<<
We are making progress on our Brainduino V0.1 assembly. We think we isolated the abnormality to IC3, the low-pass filters. We observe 10,000x gain out of the first two rounds of amplification. Our understanding is that 10,000x gain amplifies a +/-50uV brain powered signal to a +/-0.5V signal, within the operational range of the Arduino ACD.
>>

Maybe not easy understanding of so many option setup.

We use option A.
OPA2111 set gain X 100.
AD8422 use as Gain X1 ( no need put R5 & R6 to AD8422 , because we use option A )

How to get X100 :
R23 (100K) / R27 (2K) = 50
R24 (100K) / R27 (2K) = 50
50 + 50 = 100

Then IC3D set gain X250

How to get X250 :
1 + (R16 (1M) / R15 (4.02K) ) = 250 (249.75622)

Total : X 100 X 250 = X 25,000

It is not X 10,000.

C15 : 1 µF makes 0.16Hz low cut ( default )
C15 : 0.1 µF makes 1.6Hz low cut.

C35 : 47 pF makes 3200 Hz high cut ( default )
C35 : 1 nF makes 160 Hz high cut

Need setup for other channel too.

[[File:Brain-duino_circuit.png]]

<<Coming out of the first and second round of filters in IC3 on both channels we're seeing our signal clipping at +/-12V. Is this expected? >>

From IC3 output, max clip is +-12V.
Usually not clip to +-12V. In case clip continue then not working.
Make sure solder jumper : SJ8 : 2-3 connect is default.

C46 1µF is option. default is put 0 ohm.

==March 08 2017==

Debugging the brainduino v0.1_foo ...

One of these resistors is not like the other.

R15 is upside down ?
A 4.02 KOhm resistor.
(John wonders if this is a correct value for setting op-amp gain anyway,
given the corresponding 1 MOhm resistor in that part of the circuit.)

R17 the same ... maybe not upside down afterall. Jade observes that neither R15 or R17 have any visible printing on the side facing up, unlike any of the many many other resistors on the board.
Hence the initial speculation as to its potential upside-downness ...

Breaking out the multimeter now ... Here we go ... What's going on with this multimeter ?

Now reads 4.01 KOhm on R15
same on R17, very good.

The feedback resistors R16 and R19:
R16: 1.001 MOhm
R19: 1.001 MOhm

C35 difficult to get consistent reading of capacitance (maybe due to rest of circuit not isolated)
C36 11.85 nanoFarads ? Also not consistent .007 nanoFarads ... 0.4 ... 2. ... 1. ... 0.006
ok, we want 0.047 nanoFarads ie 47 picoFarads as specified in schematic
but we never see that ...
but that's okay, because measuring capacitance in circuit is a fool's errand.

backing up a bit ...
R3 tests within 2% of 1 MegaOhm despite being GROSSLY OFF CENTER
R4 1.000 MegaOhm feels more solid than R3

should we resolder R3 ?

John would (if not feeling adventurous) read documentation
and study IC 1b and IC 1a ...

Dan, feeling adventurous, reads Masahiro's instructables article
which is full of win, and yields A CLUE as per corner frequency of the
EIGHT POLE BUTTERWORTH FILTER: "1/100 of clock is filter high cut frequency."

==December 21 2016==
Understanding the brainduino v0.1

Power supplies, voltage/current regulator. voltage reference

LT1761 - 100mA, Low Noise, LDO Micropower Regulators
(IC7) (5 Volts for DC offset, 2.5 V after voltage divider,
feeds + signal inputs of 2 different op amps, also provides 5 V reference for Arduino)

RB-0512 - +/- 12 V for op amp (RB-0512 D/P -- recom 12v dc/dc converter)
(drives all op amps, also feeds LT1761)

Voltage divider (cf LT1761) built around SJ10, SJ11
a voltage divider for each channel ...
also linked with capacitors (two 10 microFarad on input, 0.1 microFarad for each output channel)

Op Amps
* for amplification (instrumentation amp setup)
* for filtering (capacitors C15 and C11

Butterworth filter

Arduino (ADC, communication with Bluetooth module)

... examining the hardware:
question about IC2 - funky solder, corrosion?
First amplifier of channel one (IC2A and IC2B)

verified IC5, IC6 not populated ...

still looking for IC7!
found it. It has some funky solder right next to it.
IC7 is tiny. Is it actually IC7?
Unlabeled solder blob next to it has white rectangle printed around it ... so maybe solder jumper

IC7 connects to C32

SJ16 is one step closer to bringing voltage divider into effect.

Can measure on blob for 5 Volts ...

There may be some test points near U$7

Some resistors from divider network identified?
R39 is 4.3 K Ohms
R34 and R35 are each 10 K Ohms ?
both contact R29 which is 2.15 K Ohms to ground

[[Image:IMG_9574.jpg|600px]]
[[Image:IMG_9573.jpg|600px]]

==December 14 2016==
what is value of c33? 50 pF or 1 nF ?
determines high-cut frequency either 160 Hz (1 nF) or 3200 Hz (50 pF)

Op amps ...

OPA2111 - what gain? expecting ~ 100 ?

what values for R27, R23, R24 ?
(also R28, R25, R26)

R27 should be 2K ohms
R23, R24 -> 100K ohms

cf instrumentation amp (figure 9 of http://www.ti.com/lit/ds/symlink/opa2111.pdf)
1 + (R23 + R24) / R27 ...
so 1 + (200 K / 2 K) = 101

so if input signal is on order of 100 microvolts
then output of first amp will be 10.100 millivolts
(really more like -50 to +50 microvolts -> -5 to +5 millivolts)

AD8422
R5 and R6 are 200 ohms
G = 1 + (19.8 kΩ/RG)
= 1 + 99 = 100

??? also see fig. 56 on page 20 of http://www.analog.com/media/en/technical-documentation/data-sheets/AD8422.pdf

now consider if input to AD8422 is on order of 10 millivolts
output will be 1.000 volts
(or if no offset voltage and input is actually ~ -5 to +5 millivolts,
then output will be -0.5 to +0.5 volts)

(however, there is a +2.5 Volt DC offset impinging on the AD2177 part of the instrumentation amp setup
... we think this is ok to say ... But is option A grounded -- no offset -- or 2.5 volts ?

where might DC offset voltage come into play?

2nd Stage AD8422 Instrumentation amp with gain resistor
2(10 kΩ + RREF)/(20 kΩ + RREF)
http://www.analog.com/media/en/technical-documentation/data-sheets/AD8422.pdf
In figure 56, you will see that an op amp is used to input to the VREF pin.

3rd stage Low-cut (aka high-pass ) filter and DC offset to 3rd Stage
OP2177ARMZ ( IC1B Ch1 and IC1A Ch2 )

4th Stage IC3D>IC3C Ch1, and IC3A>IC3B, Potentially considered a two-pole high-cut(aka low-pass)

Next week: verify 101 x gain, 100 x gain, DC offset !

Talk:DreamTeam/Brainduino

2017-03-22T03:06:12Z

198.27.140.180: March 21 2017

==March 21 2017==
Q&A with Masahiro!
<<
We are making progress on our Brainduino V0.1 assembly. We think we isolated the abnormality to IC3, the low-pass filters. We observe 10,000x gain out of the first two rounds of amplification. Our understanding is that 10,000x gain amplifies a +/-50uV brain powered signal to a +/-0.5V signal, within the operational range of the Arduino ACD.
>>

Maybe not easy understanding of so many option setup.

We use option A.
OPA2111 set gain X 100.
AD8422 use as Gain X1 ( no need put R5 & R6 to AD8422 , because we use option A )

How to get X100 :
R23 (100K) / R27 (2K) = 50
R24 (100K) / R27 (2K) = 50
50 + 50 = 100

Then IC3D set gain X250

How to get X250 :
1 + (R16 (1M) / R15 (4.02K) ) = 250 (249.75622)

Total : X 100 X 250 = X 25,000

It is not X 10,000.

C15 : 1 µF makes 0.16Hz low cut ( default )
C15 : 0.1 µF makes 1.6Hz low cut.

C35 : 47 pF makes 3200 Hz high cut ( default )
C35 : 1 nF makes 160 Hz high cut

Need setup for other channel too.

[[File:Brain-duino_circuit.png]]

<<Coming out of the first and second round of filters in IC3 on both channels we're seeing our signal clipping at +/-12V. Is this expected? >>

From IC3 output, max clip is +-12V.
Usually not clip to +-12V. In case clip continue then not working.
Make sure solder jumper : SJ8 : 2-3 connect is default.

C46 1µF is option. default is put 0 ohm.

==March 08 2017==

Debugging the brainduino v0.1_foo ...

One of these resistors is not like the other.

R15 is upside down ?
A 4.02 KOhm resistor.
(John wonders if this is a correct value for setting op-amp gain anyway,
given the corresponding 1 MOhm resistor in that part of the circuit.)

R17 the same ... maybe not upside down afterall. Jade observes that neither R15 or R17 have any visible printing on the side facing up, unlike any of the many many other resistors on the board.
Hence the initial speculation as to its potential upside-downness ...

Breaking out the multimeter now ... Here we go ... What's going on with this multimeter ?

Now reads 4.01 KOhm on R15
same on R17, very good.

The feedback resistors R16 and R19:
R16: 1.001 MOhm
R19: 1.001 MOhm

C35 difficult to get consistent reading of capacitance (maybe due to rest of circuit not isolated)
C36 11.85 nanoFarads ? Also not consistent .007 nanoFarads ... 0.4 ... 2. ... 1. ... 0.006
ok, we want 0.047 nanoFarads ie 47 picoFarads as specified in schematic
but we never see that ...
but that's okay, because measuring capacitance in circuit is a fool's errand.

backing up a bit ...
R3 tests within 2% of 1 MegaOhm despite being GROSSLY OFF CENTER
R4 1.000 MegaOhm feels more solid than R3

should we resolder R3 ?

John would (if not feeling adventurous) read documentation
and study IC 1b and IC 1a ...

Dan, feeling adventurous, reads Masahiro's instructables article
which is full of win, and yields A CLUE as per corner frequency of the
EIGHT POLE BUTTERWORTH FILTER: "1/100 of clock is filter high cut frequency."

==December 21 2016==
Understanding the brainduino v0.1

Power supplies, voltage/current regulator. voltage reference

LT1761 - 100mA, Low Noise, LDO Micropower Regulators
(IC7) (5 Volts for DC offset, 2.5 V after voltage divider,
feeds + signal inputs of 2 different op amps, also provides 5 V reference for Arduino)

RB-0512 - +/- 12 V for op amp (RB-0512 D/P -- recom 12v dc/dc converter)
(drives all op amps, also feeds LT1761)

Voltage divider (cf LT1761) built around SJ10, SJ11
a voltage divider for each channel ...
also linked with capacitors (two 10 microFarad on input, 0.1 microFarad for each output channel)

Op Amps
* for amplification (instrumentation amp setup)
* for filtering (capacitors C15 and C11

Butterworth filter

Arduino (ADC, communication with Bluetooth module)

... examining the hardware:
question about IC2 - funky solder, corrosion?
First amplifier of channel one (IC2A and IC2B)

verified IC5, IC6 not populated ...

still looking for IC7!
found it. It has some funky solder right next to it.
IC7 is tiny. Is it actually IC7?
Unlabeled solder blob next to it has white rectangle printed around it ... so maybe solder jumper

IC7 connects to C32

SJ16 is one step closer to bringing voltage divider into effect.

Can measure on blob for 5 Volts ...

There may be some test points near U$7

Some resistors from divider network identified?
R39 is 4.3 K Ohms
R34 and R35 are each 10 K Ohms ?
both contact R29 which is 2.15 K Ohms to ground

[[Image:IMG_9574.jpg|600px]]
[[Image:IMG_9573.jpg|600px]]

==December 14 2016==
what is value of c33? 50 pF or 1 nF ?
determines high-cut frequency either 160 Hz (1 nF) or 3200 Hz (50 pF)

Op amps ...

OPA2111 - what gain? expecting ~ 100 ?

what values for R27, R23, R24 ?
(also R28, R25, R26)

R27 should be 2K ohms
R23, R24 -> 100K ohms

cf instrumentation amp (figure 9 of http://www.ti.com/lit/ds/symlink/opa2111.pdf)
1 + (R23 + R24) / R27 ...
so 1 + (200 K / 2 K) = 101

so if input signal is on order of 100 microvolts
then output of first amp will be 10.100 millivolts
(really more like -50 to +50 microvolts -> -5 to +5 millivolts)

AD8422
R5 and R6 are 200 ohms
G = 1 + (19.8 kΩ/RG)
= 1 + 99 = 100

??? also see fig. 56 on page 20 of http://www.analog.com/media/en/technical-documentation/data-sheets/AD8422.pdf

now consider if input to AD8422 is on order of 10 millivolts
output will be 1.000 volts
(or if no offset voltage and input is actually ~ -5 to +5 millivolts,
then output will be -0.5 to +0.5 volts)

(however, there is a +2.5 Volt DC offset impinging on the AD2177 part of the instrumentation amp setup
... we think this is ok to say ... But is option A grounded -- no offset -- or 2.5 volts ?

where might DC offset voltage come into play?

2nd Stage AD8422 Instrumentation amp with gain resistor
2(10 kΩ + RREF)/(20 kΩ + RREF)
http://www.analog.com/media/en/technical-documentation/data-sheets/AD8422.pdf
In figure 56, you will see that an op amp is used to input to the VREF pin.

3rd stage Low-cut (aka high-pass ) filter and DC offset to 3rd Stage
OP2177ARMZ ( IC1B Ch1 and IC1A Ch2 )

4th Stage IC3D>IC3C Ch1, and IC3A>IC3B, Potentially considered a two-pole high-cut(aka low-pass)

Next week: verify 101 x gain, 100 x gain, DC offset !