Noisebridge - User contributions [en]

DreamTeam/Reading

2017-03-29T23:58:21Z

Yeled: /* random tangents */

(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"

== 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://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]

DreamTeam/Reading

2017-03-29T23:46:26Z

Yeled: /* Bayesian Inference */

(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"

== 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://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.

'''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]

File:Perception is in the Details12.pdf

2017-03-29T23:44:42Z

Yeled: Bayesian theory explaining what psychedelics do to the brain, still under review

Bayesian theory explaining what psychedelics do to the brain, still under review

DreamTeam/Reading

2017-03-29T23:34:14Z

Yeled: /* Bayesian Inference */

(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 ==

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"

== 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://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.

'''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]