DreamTeam/Reading

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(Computational Cognitive Neuroscience)
 
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(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)
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(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)  
  
==Current Discussion==
+
https://metacademy.org/
(4 September 2013) more on analysis methods:
+
-- machine learning knowledge graph
 +
 
 +
http://www.thetalkingmachines.com/ -- podcast
 +
 
 +
== 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"
 +
 
 +
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"
 +
 
 +
== 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
 +
 
 +
== 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
 
http://www.dspguide.com/ch8/1.htm

Latest revision as of 20:48, 27 April 2016

(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

Contents

[edit] 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"

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

[edit] Text Generation

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

[edit] 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"

[edit] 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"

[edit] Music

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

[edit] Code

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

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

[edit] 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"

[edit] 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"

[edit] 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"

[edit] 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/

[edit] 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"

[edit] Support Vector Machines

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

[edit] 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"

[edit] 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"

[edit] 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?"

[edit] 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"

[edit] 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"

[edit] Neural Synchrony

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

[edit] 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

[edit] 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"

[edit] 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 Hinton 2006 on Hadoop

http://www.sciencemag.org/content/suppl/2006/08/04/313.5786.504.DC1/Hinton.SOM.pdf -- supplementary material for 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"

[edit] 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

[edit] 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"

[edit] 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"

[edit] 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

[edit] (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 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)


OpenSource Machine Learning Algs from NG @MIT
Consumer grade EEG used to see "P300" reponse and for thoes with a short attention span 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."


art, dream, and eeg


mind v brain, hobson v solms
http://en.wikipedia.org/wiki/Activation-synthesis_hypothesis
File:HobsomREMDreamProtoconsciousness.pdf

"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

Gallant Group
Walker Group
Palmer Group

[edit] Sleep Research

Comment on the AASM Manual for the Scoring of Sleep and Associated Events

[edit] 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:


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

[edit] Previously

Masahiro's EEG Device/IBVA Software

and ... open source hardware design and kits on instructables.com

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.

[edit] 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/

[edit] NeuroSky Docs

File:NeuroSkyDongleProtocol.pdf

File:NeuroSkyCommunicationsProtocol.pdf

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