https://www.noisebridge.net/api.php?action=feedcontributions&user=104.11.211.148&feedformat=atomNoisebridge - User contributions [en]2024-03-29T01:37:56ZUser contributionsMediaWiki 1.39.4https://www.noisebridge.net/index.php?title=DreamTeam/Reading&diff=51700DreamTeam/Reading2016-04-13T06:13:10Z<p>104.11.211.148: /* Computational Cognitive Neuroscience */</p>
<hr />
<div>(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) <br />
<br />
https://metacademy.org/<br />
-- machine learning knowledge graph<br />
<br />
http://www.thetalkingmachines.com/ -- podcast<br />
<br />
== Computational Cognitive Neuroscience ==<br />
<br />
http://www.pnas.org/content/110/41/16390.full -- "Indirection and symbol-like processing in the prefrontal cortex and basal ganglia"<br />
<br />
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"<br />
<br />
== Text Generation ==<br />
<br />
http://www.cs.toronto.edu/~ilya/pubs/2011/LANG-RNN.pdf -- "Generating Text with Recurrent Neural Networks"<br />
<br />
== Large Scale Brain Simulation ==<br />
<br />
http://www.nowere.net/b/arch/96550/src/1378907656268.pdf -- "A world survey of artificial brain projects, Part I: Large-scale brain simulations"<br />
<br />
== Music ==<br />
<br />
http://cmr.soc.plymouth.ac.uk/publications/bci-wkshop.pdf -- "ON GENERATING EEG FOR CONTROLLING MUSICAL SYSTEMS"<br />
<br />
== Code ==<br />
<br />
https://github.com/nbdt/gotrain (our ANN code)<br />
<br />
https://github.com/nbdt/openbci-golang-server (EEG data acquisition and visualization software for OpenBCI)<br />
<br />
== Hidden Markov Models ==<br />
<br />
http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf<br />
-- "A Revealing Introduction to Hidden Markov Models"<br />
<br />
http://www.jelmerborst.nl/pubs/Borst2013b.pdf<br />
-- "Discovering Processing Stages by combining EEG with Hidden Markov Models"<br />
<br />
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf<br />
-- "A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"<br />
<br />
http://www.schalklab.org/sites/default/files/misc/Coupled%20hidden%20markov%20model%20for%20electrocorticographic%20signal%20classification.pdf<br />
-- "Coupled Hidden Markov Model for Electrocorticographic Signal Classification"<br />
<br />
== Long Short Term Memory ==<br />
<br />
http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf<br />
-- "Long Short-Term Memory"<br />
<br />
ftp://ftp.idsia.ch/pub/juergen/2002_icannMusic.pdf<br />
-- "Learning The Long-Term Structure of the Blues"<br />
<br />
http://www.overcomplete.net/papers/nn2012.pdf<br />
-- "A generalized LSTM-like training algorithm for second-order recurrent neural networks"<br />
<br />
http://www.iro.umontreal.ca/~eckdoug/papers/2003_nn.pdf<br />
-- "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets"<br />
<br />
http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html<br />
-- "Long Short-Term Memory dramatically improves Google Voice etc"<br />
<br />
== Question Answering ==<br />
<br />
http://www.overcomplete.net/papers/bica2012.pdf<br />
-- "Neural Architectures for Learning to Answer Questions"<br />
<br />
https://cs.umd.edu/~miyyer/pubs/2014_qb_rnn.pdf<br />
-- "A Neural Network for Factoid Question Answering over Paragraphs"<br />
<br />
http://arxiv.org/pdf/1502.05698.pdf<br />
-- "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"<br />
<br />
http://www.peterschueller.com/pub/2014/2014_winograd_schemas_relevance_knowledge_graphs.pdf<br />
-- "Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs"<br />
<br />
http://ijcai.org/papers15/Papers/IJCAI15-190.pdf<br />
-- "Towards Addressing the Winograd Schema Challenge — Building and Using a Semantic Parser and a Knowledge Hunting Module"<br />
<br />
http://arxiv.org/pdf/1506.05869v2.pdf<br />
-- "A Neural Conversational Model"<br />
<br />
http://arxiv.org/pdf/1508.05508v1.pdf<br />
-- "Towards Neural Network-based Reasoning"<br />
<br />
http://www.visualqa.org/vqa_iccv2015.pdf<br />
-- "VQA: Visual Question Answering"<br />
<br />
== Propagators ==<br />
Cells must support three operations:<br />
*add some content<br />
*collect the content currently accumulated<br />
*register a propagator to be notified when the accumulated content changes<br />
*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.<br />
*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.<br />
*http://web.mit.edu/~axch/www/art.pdf *http://groups.csail.mit.edu/mac/users/gjs/propagators/<br />
*http://dustycloud.org/blog/sussman-on-ai/<br />
<br />
== Boosting ==<br />
<br />
http://www.cs.princeton.edu/courses/archive/spr08/cos424/readings/Schapire2003.pdf<br />
-- "The Boosting Approach to Machine Learning An Overview"<br />
<br />
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.9955&rep=rep1&type=pdf<br />
-- "Ensembling Neural Networks: Many Could Be Better Than All"<br />
<br />
http://www.cs.columbia.edu/~rocco/Public/mlj9.pdf<br />
-- "Random Classification Noise Defeats All Convex Potential Boosters"<br />
<br />
== Support Vector Machines ==<br />
<br />
http://research.microsoft.com/en-us/um/people/cburges/papers/svmtutorial.pdf<br />
-- "A Tutorial on Support Vector Machines for Pattern Recognition"<br />
<br />
== Wire Length / Small World Networks ==<br />
<br />
http://koulakovlab.cshl.edu/publications/koulakov_chklovskii_2000.pdf<br />
-- "A wire length minimization approach to ocular dominance patterns in mammalian visual cortex"<br />
<br />
http://papers.nips.cc/paper/1910-foundations-for-a-circuit-complexity-theory-of-sensory-processing.pdf<br />
-- "Foundations for a Circuit Complexity Theory of Sensory Processing"<br />
<br />
https://www.nada.kth.se/~cjo/documents/small_world.pdf<br />
-- "Small-World Connectivity and Attractor Neural Networks"<br />
<br />
http://www.doc.ic.ac.uk/~mpsha/ShanahanPhysRevEPreprint.pdf<br />
-- "The Dynamical Complexity of Small-World Networks of Spiking Neurons"<br />
<br />
http://www.dam.brown.edu/people/elie/papers/small_world.pdf<br />
-- "Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons"<br />
<br />
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.4501&rep=rep1&type=pdf<br />
-- "Transition from Random to Small-World Neural Networks by STDP Learning Rule"<br />
<br />
http://tamar.tau.ac.il/~eshel/papers/J_Neural_Engineering.pdf<br />
-- "Compact self-wiring in cultured neural networks"<br />
<br />
== Backpropagation ==<br />
<br />
http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf -- "Neural Networks - A Systematic Introduction"<br />
<br />
http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf (works through simple example)<br />
<br />
http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/<br />
<br />
http://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf<br />
<br />
also see http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf (entire book "Neural Networks - a Systemic Introduction" by Raul Rojas)<br />
<br />
http://work.caltech.edu/lectures.html Hoeffding's inequality, VC Dimension and Back Propagation ANN<br />
<br />
http://www.cs.stir.ac.uk/research/publications/techreps/pdf/TR148.pdf ("Learning XOR: exploring the space of a classic problem")<br />
<br />
http://www.sysc.pdx.edu/classes/Werbos-Backpropagation%20through%20time.pdf<br />
-- "Backpropagation Through Time: What it Does and How to Do It"<br />
<br />
== Convolutional Neural Networks ==<br />
<br />
http://scs.ryerson.ca/~aharley/vis/harley_vis_isvc15.pdf -- "An Interactive Node-Link Visualization<br />
of Convolutional Neural Networks"<br />
<br />
http://white.stanford.edu/teach/index.php/An_Introduction_to_Convolutional_Neural_Networks<br />
<br />
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"<br />
<br />
http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf<br />
-- "What's Wrong With Deep Learning?"<br />
<br />
== Computer Vision ==<br />
<br />
http://www.cv-foundation.org/openaccess/CVPR2015.py -- some interesting papers here<br />
<br />
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"<br />
<br />
== Visual Perception (Biological Systems) ==<br />
<br />
http://cbcl.mit.edu/publications/ps/Serre_etal_PBR07.pdf<br />
-- "A quantitative theory of immediate visual recognition"<br />
<br />
http://www.dam.brown.edu/ptg/REPORTS/Invariance.pdf<br />
-- "Invariance and Selectivity in the Ventral Visual Pathway"<br />
<br />
http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf<br />
-- "Hierarchical Bayesian inference in the visual cortex"<br />
<br />
== Neural Synchrony ==<br />
<br />
http://arxiv.org/pdf/1312.6115.pdf<br />
-- "Neuronal Synchrony in Complex-Valued Deep Networks"<br />
<br />
== Spiking Neural Networks ==<br />
<br />
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"<br />
<br />
http://dai.fmph.uniba.sk/~benus/confers/CNGM_ICANN05.pdf -- "Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques"<br />
<br />
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3902 -- "Pattern Recognition in a Bucket"<br />
<br />
http://www.igi.tugraz.at/maass/psfiles/221.pdf -- "Noise as a Resource for Computation and Learning in Spiking Neural Networks"<br />
<br />
http://web.stanford.edu/group/brainsinsilicon/goals.html -- Neurogrid<br />
<br />
http://apt.cs.manchester.ac.uk/projects/SpiNNaker/ -- SpiNNaker<br />
<br />
http://personalpages.manchester.ac.uk/staff/javier.navaridas/pubs/2012.jpdc.pdf -- more on SpiNNaker<br />
<br />
==Hierarchical Temporal Memory==<br />
<br />
https://github.com/numenta/nupic/wiki/Hierarchical-Temporal-Memory-Theory<br />
<br />
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000532 -- "Towards a Mathematical Theory of Cortical Micro-circuits"<br />
<br />
==Distributed Neural Networks==<br />
<br />
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<br />
<br />
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]<br />
<br />
http://www.cs.otago.ac.nz/staffpriv/hzy/papers/pdcs03.pdf -- "Parallelization of a Backpropagation Neural Network on a Cluster Computer"<br />
<br />
http://arxiv.org/pdf/1404.5997v2.pdf -- "One weird trick for parallelizing convolutional neural networks"<br />
<br />
http://www.ics.uci.edu/~jmoorkan/pub/gpusnn-ijcnn.pdf -- "Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors"<br />
<br />
==Hopfield nets and RBMs==<br />
<br />
http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf -- Yoshua Bengio, 2009, Learning Deep Architectures for AI<br />
<br />
http://deeplearning.cs.cmu.edu/ -- Syllabus for cs course on deep learning, possible source of literature for the library <br />
<br />
https://class.coursera.org/neuralnets-2012-001/lecture -- Lecture 11 - 15 to aid in understanding of Hinton 2006 paper above<br />
<br />
http://www.pnas.org/content/79/8/2554.full.pdf -- "Neural networks and physical systems with emergent collective computational abilities" (Hopfield 1982)<br />
<br />
http://page.mi.fu-berlin.de/rojas/neural/chapter/K13.pdf -- "The Hopfield Model" (Rojas 1996)<br />
<br />
http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"<br />
<br />
http://www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/cameraready_xwjiabibe.pdf -- "A Novel Semi-supervised Deep Learning Framework<br />
for Affective State Recognition on EEG Signals"<br />
<br />
http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf -- "A Practical Guide to Training Restricted Boltzmann Machines"<br />
<br />
http://arxiv.org/pdf/1503.07793v2.pdf<br />
-- "Gibbs Sampling with Low-Power Spiking Digital Neurons"<br />
<br />
http://arxiv.org/pdf/1311.0190v1 -- "On the typical properties of inverse problems in statistical mechanics" Iacopo Mastromatteo 2013<br />
<br />
== Markov Chain Monte Carlo ==<br />
<br />
http://www.cs.princeton.edu/courses/archive/spr06/cos598C/papers/AndrieuFreitasDoucetJordan2003.pdf<br />
-- "An Introduction to MCMC for Machine Learning"<br />
<br />
http://jmlr.org/proceedings/papers/v37/salimans15.pdf<br />
-- "Markov Chain Monte Carlo and Variational Inference: Bridging the Gap"<br />
<br />
==Entrainment==<br />
<br />
http://www.scirp.org/Journal/PaperDownload.aspx?paperID=33251 -- "Alpha Rhythms Response to 10 Hz Flicker Is Wavelength Dependent"<br />
<br />
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"<br />
<br />
http://www.jneurosci.org/content/23/37/11621.full.pdf -- "Human Cerebral Activation during Steady-State Visual-Evoked Responses"<br />
<br />
http://www.dauwels.com/Papers/CogDyn%202009.pdf -- "On the synchrony of steady state visual evoked potentials and oscillatory burst events"<br />
<br />
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"<br />
<br />
==Mining Scientific Literature==<br />
<br />
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<br />
<br />
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674139/pdf/pcbi.1004630.pdf -- "Text Mining for Protein Docking" Badal 2015 PLoS Comput Biol.<br />
<br />
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<br />
<br />
==(not necessarilly very) Current Discussion==<br />
<br />
re Tononi's "Integrated Information Theory" http://www.scottaaronson.com/blog/?p=1799<br />
<br />
(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:<br />
<br />
http://www.biosemi.com/publications/pdf/Bierman.pdf -- Wave function collapse<br />
<br />
[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999<br />
<br />
[[File:CoherentEEGAmbiguousFigureBinding.pdf]] -- "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" -- Klemm, Li, and Hernandez 2000 <br />
<br />
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.<br />
<br />
Here is an article that looks more directly at visual evoked potential measures:<br />
<br />
[[File:ERP_Stereoscopic.pdf]] -- "Evoked Responses to Distinct and Nebulous Stereoscopic Stimuli" -- Dunlop et al 1983<br />
<br />
<br />
(11 September 2013) more on analysis methods:<br />
<br />
http://slesinsky.org/brian/misc/eulers_identity.html<br />
<br />
http://www.dspguide.com/ch8/1.htm<br />
<br />
[[File:Fftw3.pdf]]<br />
<br />
[[File:ParametricEEGAnalysis.pdf]]<br />
<br />
[[File:ICATutorial.pdf]]<br />
<br />
[[File:ICAFrequencyDomainEEG.pdf]]<br />
<br />
<br />
(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!<br />
<br />
Would be good to identify any papers suitable for more in-depth study. Currently have a wide field to graze for selections:<br />
<br />
[[File:DWTandFFTforEEG.pdf]] "EEG Classifier using Fourier Transform and Wavelet Transform" -- Maan Shaker, 2007<br />
<br />
[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999<br />
<br />
[[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''<br />
<br />
[[File:ContinuousAndDiscreteWaveletTransforms.pdf]] -- review of (pre-1990) wavelet literature -- Christopher Heil and David Walnut, 1989<br />
<br />
[[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<br />
<br />
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)<br />
<br />
----<br />
<br />
[http://www.nickgillian.com/software/grt OpenSource Machine Learning Algs from NG @MIT]<br />
<br>[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]<br />
<br>(discussed at meetup Wednesday 31 July 2013)<br />
<br>"Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" Klemm, Li, and Hernandez 2000 <br />
<br>[[File:CoherentEEGAmbiguousFigureBinding.pdf]]<br />
<br>"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."<br />
<br><br />
<br />
----<br />
<br />
[http://dreamsessions.net art, dream, and eeg]<br />
<br />
----<br />
<br />
[http://www.believermag.com/issues/200710/?read=article_aviv mind v brain, hobson v solms]<br />
<br>http://en.wikipedia.org/wiki/Activation-synthesis_hypothesis<br />
<br>[[File:HobsomREMDreamProtoconsciousness.pdf|Hobson09ProtosconsciousnessREMDream]]<br />
<br />
"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."<br />
<br />
----<br />
<br />
Berkeley Labs<br />
<br />
[http://gallantlab.org/index.html Gallant Group]<br />
<br>[http://walkerlab.berkeley.edu/ Walker Group]<br />
<br>[http://socrates.berkeley.edu/~plab/ Palmer Group]<br />
<br />
==Sleep Research==<br />
<br />
[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2335403/ Comment on the AASM Manual for the Scoring of Sleep and Associated Events]<br />
<br />
==random tangents==<br />
(following previous discussion) - we might select a few to study in more depth<br />
(... or not! Plenty more to explore - suggestions (random or otherwise) are welcome.<br />
<br />
'''stereoscopic perception:'''<br />
*[[File:ERP_Stereoscopic.pdf]] <br />
<br />
<br />
some (maybe) interesting background on Information Theory (cool title...)<br />
Claude Shannon: "Communication in the Presence of Noise"<br />
[[File:Shannon_noise.pdf]]<br />
"We will call a system that transmits without errors at the rate ''C'' an ideal system.<br />
Such a system cannot be achieved with any finite encoding process<br />
but can be approximated as closely as desired."<br />
<br />
wikipedia etc quick reads:<br />
https://en.wikipedia.org/wiki/Eeg<br />
https://en.wikipedia.org/wiki/Neural_synchronization<br />
https://en.wikipedia.org/wiki/Event-related_potentials<br />
http://www.scholarpedia.org/article/Spike-and-wave_oscillations<br />
http://www.scholarpedia.org/article/Thalamocortical_oscillations<br />
<br />
==Previously==<br />
<br />
[http://www.psychiclab.net/ Masahiro's EEG Device/IBVA Software]<br />
<br />
[http://www.instructables.com/id/open-brain-wave-interface-hardware-1/ and ... open source hardware design and kits on instructables.com]<br />
<br />
[http://brainstorms.puzzlebox.info/ Puzzlebox - Opensource BCI Developers]<br />
<br />
Morgan from GazzLab @ MissionBay/UCSF<br />
<br />
<br />
https://github.com/gazzlab<br />
<br />
Let's ease into a lightweight "journal club" discussion with this technical report from NeuroSky.<br />
<br />
Name: A user-friendly SSVEP-based brain-computer interface using a time-domain classifier, Luo A and Sullivan TJ 2010<br />
<br />
URL: [[File:NeuroSkyVEP.pdf]]<br />
<br />
Please add your comments & questions here.<br />
<br />
==Background Reading==<br />
<br />
http://nanosouffle.net/ (view into Arxiv.org)<br />
<br />
Name: Hunting for Meaning after Midnight, Miller 2007<br />
<br />
URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0002.pdf><br />
<br />
Name: Broken mirrors, Ram, VS, & Oberman, LM, 2006, Nov<br />
<br />
URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0003.pdf><br />
<br />
Ramachandran Critique<br />
<br />
http://blogs.scientificamerican.com/guest-blog/2012/11/06/whats-so-special-about-mirror-neurons/<br />
<br />
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773693/<br />
<br />
Sleep/Dream Studies<br />
<br />
http://www.cns.atr.jp/dni/en/publications/<br />
<br />
==NeuroSky Docs==<br />
[[File:NeuroSkyDongleProtocol.pdf]]<br />
<br />
[[File:NeuroSkyCommunicationsProtocol.pdf]]</div>104.11.211.148https://www.noisebridge.net/index.php?title=NOPE_-_Toorcamp_2016&diff=51429NOPE - Toorcamp 20162016-03-14T03:08:28Z<p>104.11.211.148: </p>
<hr />
<div>[http://toorcamp.toorcon.net/ Toorcamp] is happening from June 8th to 12th is the Orcas Islands of northwest Washington State! A bunch of Noisebridgers and friends are headed and we want to make a noisebridge camp happen!<br />
<br />
== What ==<br />
<br />
Toorcamp is a multi-day community gathering of hackers from across America and elsewhere coming together to share talks, hack things and have fun.<br />
<br />
== Where ==<br />
<br />
The Doe Bay Resort in the Orcas Islands on the northwest coast of Wasthington State.<br />
<br />
== When ==<br />
<br />
June 8th to 12th 2016<br />
<br />
== Who ==<br />
<br />
Are you headed to toorcamp? You should put your name down on the list below:<br />
<br />
* [[User:Patrickod]]<br />
* [[User:Maltman23]]<br />
* [[User:Miloh]]<br />
*[[User:bfb]]<br />
* [[User:Rubin110]]<br />
* You?!?<br />
<br />
== Transport ==<br />
<br />
Getting a bunch of hacker gear to the Orcas Islands will be no easy thing. If you have a vehicle which you're thinking of driving up to WA in and want to turn your trip into a hacker roadtrip then post your name below and let's make that happen!<br />
<br />
Alternatively it's possible to fly to nearby airports, but from there it's still a multi-hour drive to the event. You can check out the [http://wiki.toorcamp.org/index.php?title=Travel wiki] for more information about traveling to camp by land, sea, and air.</div>104.11.211.148https://www.noisebridge.net/index.php?title=DreamTeam/Reading&diff=50342DreamTeam/Reading2015-12-07T03:04:07Z<p>104.11.211.148: /* Hopfield nets and RBMs */</p>
<hr />
<div>(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) <br />
<br />
https://metacademy.org/<br />
-- machine learning knowledge graph<br />
<br />
http://www.thetalkingmachines.com/ -- podcast<br />
<br />
== Code ==<br />
<br />
https://github.com/nbdt/gotrain (our ANN code)<br />
<br />
https://github.com/nbdt/openbci-golang-server (EEG data acquisition and visualization software for OpenBCI)<br />
<br />
== Hidden Markov Models ==<br />
<br />
http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf<br />
-- "A Revealing Introduction to Hidden Markov Models"<br />
<br />
http://www.jelmerborst.nl/pubs/Borst2013b.pdf<br />
-- "Discovering Processing Stages by combining EEG with Hidden Markov Models"<br />
<br />
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf<br />
-- "A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"<br />
<br />
http://www.schalklab.org/sites/default/files/misc/Coupled%20hidden%20markov%20model%20for%20electrocorticographic%20signal%20classification.pdf<br />
-- "Coupled Hidden Markov Model for Electrocorticographic Signal Classification"<br />
<br />
== Long Short Term Memory ==<br />
<br />
http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf<br />
-- "Long Short-Term Memory"<br />
<br />
ftp://ftp.idsia.ch/pub/juergen/2002_icannMusic.pdf<br />
-- "Learning The Long-Term Structure of the Blues"<br />
<br />
http://www.overcomplete.net/papers/nn2012.pdf<br />
-- "A generalized LSTM-like training algorithm for second-order recurrent neural networks"<br />
<br />
http://www.iro.umontreal.ca/~eckdoug/papers/2003_nn.pdf<br />
-- "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets"<br />
<br />
http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html<br />
-- "Long Short-Term Memory dramatically improves Google Voice etc"<br />
<br />
== Question Answering ==<br />
<br />
http://www.overcomplete.net/papers/bica2012.pdf<br />
-- "Neural Architectures for Learning to Answer Questions"<br />
<br />
https://cs.umd.edu/~miyyer/pubs/2014_qb_rnn.pdf<br />
-- "A Neural Network for Factoid Question Answering over Paragraphs"<br />
<br />
http://arxiv.org/pdf/1502.05698.pdf<br />
-- "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"<br />
<br />
http://www.peterschueller.com/pub/2014/2014_winograd_schemas_relevance_knowledge_graphs.pdf<br />
-- "Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs"<br />
<br />
http://ijcai.org/papers15/Papers/IJCAI15-190.pdf<br />
-- "Towards Addressing the Winograd Schema Challenge — Building and Using a Semantic Parser and a Knowledge Hunting Module"<br />
<br />
http://arxiv.org/pdf/1506.05869v2.pdf<br />
-- "A Neural Conversational Model"<br />
<br />
http://arxiv.org/pdf/1508.05508v1.pdf<br />
-- "Towards Neural Network-based Reasoning"<br />
<br />
http://www.visualqa.org/vqa_iccv2015.pdf<br />
-- "VQA: Visual Question Answering"<br />
<br />
== Propagators ==<br />
Cells must support three operations:<br />
*add some content<br />
*collect the content currently accumulated<br />
*register a propagator to be notified when the accumulated content changes<br />
*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.<br />
*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.<br />
*http://web.mit.edu/~axch/www/art.pdf *http://groups.csail.mit.edu/mac/users/gjs/propagators/<br />
*http://dustycloud.org/blog/sussman-on-ai/<br />
<br />
== Boosting ==<br />
<br />
http://www.cs.princeton.edu/courses/archive/spr08/cos424/readings/Schapire2003.pdf<br />
-- "The Boosting Approach to Machine Learning An Overview"<br />
<br />
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.9955&rep=rep1&type=pdf<br />
-- "Ensembling Neural Networks: Many Could Be Better Than All"<br />
<br />
http://www.cs.columbia.edu/~rocco/Public/mlj9.pdf<br />
-- "Random Classification Noise Defeats All Convex Potential Boosters"<br />
<br />
== Support Vector Machines ==<br />
<br />
http://research.microsoft.com/en-us/um/people/cburges/papers/svmtutorial.pdf<br />
-- "A Tutorial on Support Vector Machines for Pattern Recognition"<br />
<br />
== Wire Length / Small World Networks ==<br />
<br />
http://koulakovlab.cshl.edu/publications/koulakov_chklovskii_2000.pdf<br />
-- "A wire length minimization approach to ocular dominance patterns in mammalian visual cortex"<br />
<br />
http://papers.nips.cc/paper/1910-foundations-for-a-circuit-complexity-theory-of-sensory-processing.pdf<br />
-- "Foundations for a Circuit Complexity Theory of Sensory Processing"<br />
<br />
https://www.nada.kth.se/~cjo/documents/small_world.pdf<br />
-- "Small-World Connectivity and Attractor Neural Networks"<br />
<br />
http://www.doc.ic.ac.uk/~mpsha/ShanahanPhysRevEPreprint.pdf<br />
-- "The Dynamical Complexity of Small-World Networks of Spiking Neurons"<br />
<br />
http://www.dam.brown.edu/people/elie/papers/small_world.pdf<br />
-- "Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons"<br />
<br />
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.4501&rep=rep1&type=pdf<br />
-- "Transition from Random to Small-World Neural Networks by STDP Learning Rule"<br />
<br />
http://tamar.tau.ac.il/~eshel/papers/J_Neural_Engineering.pdf<br />
-- "Compact self-wiring in cultured neural networks"<br />
<br />
== Backpropagation ==<br />
<br />
http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf (works through simple example)<br />
<br />
http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/<br />
<br />
http://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf<br />
<br />
also see http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf (entire book "Neural Networks - a Systemic Introduction" by Raul Rojas)<br />
<br />
http://work.caltech.edu/lectures.html Hoeffding's inequality, VC Dimension and Back Propagation ANN<br />
<br />
http://www.cs.stir.ac.uk/research/publications/techreps/pdf/TR148.pdf ("Learning XOR: exploring the space of a classic problem")<br />
<br />
http://www.sysc.pdx.edu/classes/Werbos-Backpropagation%20through%20time.pdf<br />
-- "Backpropagation Through Time: What it Does and How to Do It"<br />
<br />
== Convolutional Neural Networks ==<br />
<br />
http://white.stanford.edu/teach/index.php/An_Introduction_to_Convolutional_Neural_Networks<br />
<br />
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"<br />
<br />
http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf<br />
-- "What's Wrong With Deep Learning?"<br />
<br />
== Computer Vision ==<br />
<br />
http://www.cv-foundation.org/openaccess/CVPR2015.py -- some interesting papers here<br />
<br />
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"<br />
<br />
== Spiking Neural Networks ==<br />
<br />
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"<br />
<br />
http://dai.fmph.uniba.sk/~benus/confers/CNGM_ICANN05.pdf -- "Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques"<br />
<br />
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3902 -- "Pattern Recognition in a Bucket"<br />
<br />
http://www.igi.tugraz.at/maass/psfiles/221.pdf -- "Noise as a Resource for Computation and Learning in Spiking Neural Networks"<br />
<br />
http://web.stanford.edu/group/brainsinsilicon/goals.html -- Neurogrid<br />
<br />
http://apt.cs.manchester.ac.uk/projects/SpiNNaker/ -- SpiNNaker<br />
<br />
http://personalpages.manchester.ac.uk/staff/javier.navaridas/pubs/2012.jpdc.pdf -- more on SpiNNaker<br />
<br />
==Hierarchical Temporal Memory==<br />
<br />
https://github.com/numenta/nupic/wiki/Hierarchical-Temporal-Memory-Theory<br />
<br />
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000532 -- "Towards a Mathematical Theory of Cortical Micro-circuits"<br />
<br />
==Distributed Neural Networks==<br />
<br />
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<br />
<br />
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]<br />
<br />
http://www.cs.otago.ac.nz/staffpriv/hzy/papers/pdcs03.pdf -- "Parallelization of a Backpropagation Neural Network on a Cluster Computer"<br />
<br />
http://arxiv.org/pdf/1404.5997v2.pdf -- "One weird trick for parallelizing convolutional neural networks"<br />
<br />
http://www.ics.uci.edu/~jmoorkan/pub/gpusnn-ijcnn.pdf -- "Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors"<br />
<br />
==Hopfield nets and RBMs==<br />
<br />
http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf -- Yoshua Bengio, 2009, Learning Deep Architectures for AI<br />
<br />
http://deeplearning.cs.cmu.edu/ -- Syllabus for cs course on deep learning, possible source of literature for the library <br />
<br />
https://class.coursera.org/neuralnets-2012-001/lecture -- Lecture 11 - 15 to aid in understanding of Hinton 2006 paper above<br />
<br />
http://www.pnas.org/content/79/8/2554.full.pdf -- "Neural networks and physical systems with emergent collective computational abilities" (Hopfield 1982)<br />
<br />
http://page.mi.fu-berlin.de/rojas/neural/chapter/K13.pdf -- "The Hopfield Model" (Rojas 1996)<br />
<br />
http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"<br />
<br />
http://www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/cameraready_xwjiabibe.pdf -- "A Novel Semi-supervised Deep Learning Framework<br />
for Affective State Recognition on EEG Signals"<br />
<br />
http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf -- "A Practical Guide to Training Restricted Boltzmann Machines"<br />
<br />
http://arxiv.org/pdf/1503.07793v2.pdf<br />
-- "Gibbs Sampling with Low-Power Spiking Digital Neurons"<br />
<br />
==Entrainment==<br />
<br />
http://www.scirp.org/Journal/PaperDownload.aspx?paperID=33251 -- "Alpha Rhythms Response to 10 Hz Flicker Is Wavelength Dependent"<br />
<br />
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"<br />
<br />
http://www.jneurosci.org/content/23/37/11621.full.pdf -- "Human Cerebral Activation during Steady-State Visual-Evoked Responses"<br />
<br />
http://www.dauwels.com/Papers/CogDyn%202009.pdf -- "On the synchrony of steady state visual evoked potentials and oscillatory burst events"<br />
<br />
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"<br />
<br />
==(not necessarilly very) Current Discussion==<br />
<br />
re Tononi's "Integrated Information Theory" http://www.scottaaronson.com/blog/?p=1799<br />
<br />
(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:<br />
<br />
http://www.biosemi.com/publications/pdf/Bierman.pdf -- Wave function collapse<br />
<br />
[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999<br />
<br />
[[File:CoherentEEGAmbiguousFigureBinding.pdf]] -- "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" -- Klemm, Li, and Hernandez 2000 <br />
<br />
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.<br />
<br />
Here is an article that looks more directly at visual evoked potential measures:<br />
<br />
[[File:ERP_Stereoscopic.pdf]] -- "Evoked Responses to Distinct and Nebulous Stereoscopic Stimuli" -- Dunlop et al 1983<br />
<br />
<br />
(11 September 2013) more on analysis methods:<br />
<br />
http://slesinsky.org/brian/misc/eulers_identity.html<br />
<br />
http://www.dspguide.com/ch8/1.htm<br />
<br />
[[File:Fftw3.pdf]]<br />
<br />
[[File:ParametricEEGAnalysis.pdf]]<br />
<br />
[[File:ICATutorial.pdf]]<br />
<br />
[[File:ICAFrequencyDomainEEG.pdf]]<br />
<br />
<br />
(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!<br />
<br />
Would be good to identify any papers suitable for more in-depth study. Currently have a wide field to graze for selections:<br />
<br />
[[File:DWTandFFTforEEG.pdf]] "EEG Classifier using Fourier Transform and Wavelet Transform" -- Maan Shaker, 2007<br />
<br />
[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999<br />
<br />
[[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''<br />
<br />
[[File:ContinuousAndDiscreteWaveletTransforms.pdf]] -- review of (pre-1990) wavelet literature -- Christopher Heil and David Walnut, 1989<br />
<br />
[[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<br />
<br />
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)<br />
<br />
----<br />
<br />
[http://www.nickgillian.com/software/grt OpenSource Machine Learning Algs from NG @MIT]<br />
<br>[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]<br />
<br>(discussed at meetup Wednesday 31 July 2013)<br />
<br>"Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" Klemm, Li, and Hernandez 2000 <br />
<br>[[File:CoherentEEGAmbiguousFigureBinding.pdf]]<br />
<br>"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."<br />
<br><br />
<br />
----<br />
<br />
[http://dreamsessions.net art, dream, and eeg]<br />
<br />
----<br />
<br />
[http://www.believermag.com/issues/200710/?read=article_aviv mind v brain, hobson v solms]<br />
<br>http://en.wikipedia.org/wiki/Activation-synthesis_hypothesis<br />
<br>[[File:HobsomREMDreamProtoconsciousness.pdf|Hobson09ProtosconsciousnessREMDream]]<br />
<br />
"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."<br />
<br />
----<br />
<br />
Berkeley Labs<br />
<br />
[http://gallantlab.org/index.html Gallant Group]<br />
<br>[http://walkerlab.berkeley.edu/ Walker Group]<br />
<br>[http://socrates.berkeley.edu/~plab/ Palmer Group]<br />
<br />
==Sleep Research==<br />
<br />
[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2335403/ Comment on the AASM Manual for the Scoring of Sleep and Associated Events]<br />
<br />
==random tangents==<br />
(following previous discussion) - we might select a few to study in more depth<br />
(... or not! Plenty more to explore - suggestions (random or otherwise) are welcome.<br />
<br />
'''stereoscopic perception:'''<br />
*[[File:ERP_Stereoscopic.pdf]] <br />
<br />
<br />
some (maybe) interesting background on Information Theory (cool title...)<br />
Claude Shannon: "Communication in the Presence of Noise"<br />
[[File:Shannon_noise.pdf]]<br />
"We will call a system that transmits without errors at the rate ''C'' an ideal system.<br />
Such a system cannot be achieved with any finite encoding process<br />
but can be approximated as closely as desired."<br />
<br />
wikipedia etc quick reads:<br />
https://en.wikipedia.org/wiki/Eeg<br />
https://en.wikipedia.org/wiki/Neural_synchronization<br />
https://en.wikipedia.org/wiki/Event-related_potentials<br />
http://www.scholarpedia.org/article/Spike-and-wave_oscillations<br />
http://www.scholarpedia.org/article/Thalamocortical_oscillations<br />
<br />
==Previously==<br />
<br />
[http://www.psychiclab.net/ Masahiro's EEG Device/IBVA Software]<br />
<br />
[http://www.instructables.com/id/open-brain-wave-interface-hardware-1/ and ... open source hardware design and kits on instructables.com]<br />
<br />
[http://brainstorms.puzzlebox.info/ Puzzlebox - Opensource BCI Developers]<br />
<br />
Morgan from GazzLab @ MissionBay/UCSF<br />
<br />
<br />
https://github.com/gazzlab<br />
<br />
Let's ease into a lightweight "journal club" discussion with this technical report from NeuroSky.<br />
<br />
Name: A user-friendly SSVEP-based brain-computer interface using a time-domain classifier, Luo A and Sullivan TJ 2010<br />
<br />
URL: [[File:NeuroSkyVEP.pdf]]<br />
<br />
Please add your comments & questions here.<br />
<br />
==Background Reading==<br />
<br />
http://nanosouffle.net/ (view into Arxiv.org)<br />
<br />
Name: Hunting for Meaning after Midnight, Miller 2007<br />
<br />
URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0002.pdf><br />
<br />
Name: Broken mirrors, Ram, VS, & Oberman, LM, 2006, Nov<br />
<br />
URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0003.pdf><br />
<br />
Ramachandran Critique<br />
<br />
http://blogs.scientificamerican.com/guest-blog/2012/11/06/whats-so-special-about-mirror-neurons/<br />
<br />
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773693/<br />
<br />
Sleep/Dream Studies<br />
<br />
http://www.cns.atr.jp/dni/en/publications/<br />
<br />
==NeuroSky Docs==<br />
[[File:NeuroSkyDongleProtocol.pdf]]<br />
<br />
[[File:NeuroSkyCommunicationsProtocol.pdf]]</div>104.11.211.148https://www.noisebridge.net/index.php?title=DreamTeam/Reading&diff=50341DreamTeam/Reading2015-12-06T22:37:24Z<p>104.11.211.148: /* Hopfield nets and RBMs */</p>
<hr />
<div>(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) <br />
<br />
https://metacademy.org/<br />
-- machine learning knowledge graph<br />
<br />
http://www.thetalkingmachines.com/ -- podcast<br />
<br />
== Code ==<br />
<br />
https://github.com/nbdt/gotrain (our ANN code)<br />
<br />
https://github.com/nbdt/openbci-golang-server (EEG data acquisition and visualization software for OpenBCI)<br />
<br />
== Hidden Markov Models ==<br />
<br />
http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf<br />
-- "A Revealing Introduction to Hidden Markov Models"<br />
<br />
http://www.jelmerborst.nl/pubs/Borst2013b.pdf<br />
-- "Discovering Processing Stages by combining EEG with Hidden Markov Models"<br />
<br />
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf<br />
-- "A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"<br />
<br />
http://www.schalklab.org/sites/default/files/misc/Coupled%20hidden%20markov%20model%20for%20electrocorticographic%20signal%20classification.pdf<br />
-- "Coupled Hidden Markov Model for Electrocorticographic Signal Classification"<br />
<br />
== Long Short Term Memory ==<br />
<br />
http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf<br />
-- "Long Short-Term Memory"<br />
<br />
ftp://ftp.idsia.ch/pub/juergen/2002_icannMusic.pdf<br />
-- "Learning The Long-Term Structure of the Blues"<br />
<br />
http://www.overcomplete.net/papers/nn2012.pdf<br />
-- "A generalized LSTM-like training algorithm for second-order recurrent neural networks"<br />
<br />
http://www.iro.umontreal.ca/~eckdoug/papers/2003_nn.pdf<br />
-- "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets"<br />
<br />
http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html<br />
-- "Long Short-Term Memory dramatically improves Google Voice etc"<br />
<br />
== Question Answering ==<br />
<br />
http://www.overcomplete.net/papers/bica2012.pdf<br />
-- "Neural Architectures for Learning to Answer Questions"<br />
<br />
https://cs.umd.edu/~miyyer/pubs/2014_qb_rnn.pdf<br />
-- "A Neural Network for Factoid Question Answering over Paragraphs"<br />
<br />
http://arxiv.org/pdf/1502.05698.pdf<br />
-- "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"<br />
<br />
http://www.peterschueller.com/pub/2014/2014_winograd_schemas_relevance_knowledge_graphs.pdf<br />
-- "Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs"<br />
<br />
http://ijcai.org/papers15/Papers/IJCAI15-190.pdf<br />
-- "Towards Addressing the Winograd Schema Challenge — Building and Using a Semantic Parser and a Knowledge Hunting Module"<br />
<br />
http://arxiv.org/pdf/1506.05869v2.pdf<br />
-- "A Neural Conversational Model"<br />
<br />
http://arxiv.org/pdf/1508.05508v1.pdf<br />
-- "Towards Neural Network-based Reasoning"<br />
<br />
http://www.visualqa.org/vqa_iccv2015.pdf<br />
-- "VQA: Visual Question Answering"<br />
<br />
== Propagators ==<br />
Cells must support three operations:<br />
*add some content<br />
*collect the content currently accumulated<br />
*register a propagator to be notified when the accumulated content changes<br />
*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.<br />
*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.<br />
*http://web.mit.edu/~axch/www/art.pdf *http://groups.csail.mit.edu/mac/users/gjs/propagators/<br />
*http://dustycloud.org/blog/sussman-on-ai/<br />
<br />
== Boosting ==<br />
<br />
http://www.cs.princeton.edu/courses/archive/spr08/cos424/readings/Schapire2003.pdf<br />
-- "The Boosting Approach to Machine Learning An Overview"<br />
<br />
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.9955&rep=rep1&type=pdf<br />
-- "Ensembling Neural Networks: Many Could Be Better Than All"<br />
<br />
http://www.cs.columbia.edu/~rocco/Public/mlj9.pdf<br />
-- "Random Classification Noise Defeats All Convex Potential Boosters"<br />
<br />
== Support Vector Machines ==<br />
<br />
http://research.microsoft.com/en-us/um/people/cburges/papers/svmtutorial.pdf<br />
-- "A Tutorial on Support Vector Machines for Pattern Recognition"<br />
<br />
== Wire Length / Small World Networks ==<br />
<br />
http://koulakovlab.cshl.edu/publications/koulakov_chklovskii_2000.pdf<br />
-- "A wire length minimization approach to ocular dominance patterns in mammalian visual cortex"<br />
<br />
http://papers.nips.cc/paper/1910-foundations-for-a-circuit-complexity-theory-of-sensory-processing.pdf<br />
-- "Foundations for a Circuit Complexity Theory of Sensory Processing"<br />
<br />
https://www.nada.kth.se/~cjo/documents/small_world.pdf<br />
-- "Small-World Connectivity and Attractor Neural Networks"<br />
<br />
http://www.doc.ic.ac.uk/~mpsha/ShanahanPhysRevEPreprint.pdf<br />
-- "The Dynamical Complexity of Small-World Networks of Spiking Neurons"<br />
<br />
http://www.dam.brown.edu/people/elie/papers/small_world.pdf<br />
-- "Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons"<br />
<br />
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.4501&rep=rep1&type=pdf<br />
-- "Transition from Random to Small-World Neural Networks by STDP Learning Rule"<br />
<br />
http://tamar.tau.ac.il/~eshel/papers/J_Neural_Engineering.pdf<br />
-- "Compact self-wiring in cultured neural networks"<br />
<br />
== Backpropagation ==<br />
<br />
http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf (works through simple example)<br />
<br />
http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/<br />
<br />
http://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf<br />
<br />
also see http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf (entire book "Neural Networks - a Systemic Introduction" by Raul Rojas)<br />
<br />
http://work.caltech.edu/lectures.html Hoeffding's inequality, VC Dimension and Back Propagation ANN<br />
<br />
http://www.cs.stir.ac.uk/research/publications/techreps/pdf/TR148.pdf ("Learning XOR: exploring the space of a classic problem")<br />
<br />
http://www.sysc.pdx.edu/classes/Werbos-Backpropagation%20through%20time.pdf<br />
-- "Backpropagation Through Time: What it Does and How to Do It"<br />
<br />
== Convolutional Neural Networks ==<br />
<br />
http://white.stanford.edu/teach/index.php/An_Introduction_to_Convolutional_Neural_Networks<br />
<br />
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"<br />
<br />
http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf<br />
-- "What's Wrong With Deep Learning?"<br />
<br />
== Computer Vision ==<br />
<br />
http://www.cv-foundation.org/openaccess/CVPR2015.py -- some interesting papers here<br />
<br />
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"<br />
<br />
== Spiking Neural Networks ==<br />
<br />
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"<br />
<br />
http://dai.fmph.uniba.sk/~benus/confers/CNGM_ICANN05.pdf -- "Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques"<br />
<br />
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3902 -- "Pattern Recognition in a Bucket"<br />
<br />
http://www.igi.tugraz.at/maass/psfiles/221.pdf -- "Noise as a Resource for Computation and Learning in Spiking Neural Networks"<br />
<br />
http://web.stanford.edu/group/brainsinsilicon/goals.html -- Neurogrid<br />
<br />
http://apt.cs.manchester.ac.uk/projects/SpiNNaker/ -- SpiNNaker<br />
<br />
http://personalpages.manchester.ac.uk/staff/javier.navaridas/pubs/2012.jpdc.pdf -- more on SpiNNaker<br />
<br />
==Hierarchical Temporal Memory==<br />
<br />
https://github.com/numenta/nupic/wiki/Hierarchical-Temporal-Memory-Theory<br />
<br />
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000532 -- "Towards a Mathematical Theory of Cortical Micro-circuits"<br />
<br />
==Distributed Neural Networks==<br />
<br />
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<br />
<br />
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]<br />
<br />
http://www.cs.otago.ac.nz/staffpriv/hzy/papers/pdcs03.pdf -- "Parallelization of a Backpropagation Neural Network on a Cluster Computer"<br />
<br />
http://arxiv.org/pdf/1404.5997v2.pdf -- "One weird trick for parallelizing convolutional neural networks"<br />
<br />
http://www.ics.uci.edu/~jmoorkan/pub/gpusnn-ijcnn.pdf -- "Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors"<br />
<br />
==Hopfield nets and RBMs==<br />
<br />
http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf -- Yoshua Bengio, 2009, Learning Deep Architectures for AI<br />
<br />
https://class.coursera.org/neuralnets-2012-001/lecture -- Lecture 11 - 15 to aid in understanding of Hinton 2006 paper above<br />
<br />
http://www.pnas.org/content/79/8/2554.full.pdf -- "Neural networks and physical systems with emergent collective computational abilities" (Hopfield 1982)<br />
<br />
http://page.mi.fu-berlin.de/rojas/neural/chapter/K13.pdf -- "The Hopfield Model" (Rojas 1996)<br />
<br />
http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"<br />
<br />
http://www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/cameraready_xwjiabibe.pdf -- "A Novel Semi-supervised Deep Learning Framework<br />
for Affective State Recognition on EEG Signals"<br />
<br />
http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf -- "A Practical Guide to Training Restricted Boltzmann Machines"<br />
<br />
http://arxiv.org/pdf/1503.07793v2.pdf<br />
-- "Gibbs Sampling with Low-Power Spiking Digital Neurons"<br />
<br />
==Entrainment==<br />
<br />
http://www.scirp.org/Journal/PaperDownload.aspx?paperID=33251 -- "Alpha Rhythms Response to 10 Hz Flicker Is Wavelength Dependent"<br />
<br />
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"<br />
<br />
http://www.jneurosci.org/content/23/37/11621.full.pdf -- "Human Cerebral Activation during Steady-State Visual-Evoked Responses"<br />
<br />
http://www.dauwels.com/Papers/CogDyn%202009.pdf -- "On the synchrony of steady state visual evoked potentials and oscillatory burst events"<br />
<br />
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"<br />
<br />
==(not necessarilly very) Current Discussion==<br />
<br />
re Tononi's "Integrated Information Theory" http://www.scottaaronson.com/blog/?p=1799<br />
<br />
(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:<br />
<br />
http://www.biosemi.com/publications/pdf/Bierman.pdf -- Wave function collapse<br />
<br />
[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999<br />
<br />
[[File:CoherentEEGAmbiguousFigureBinding.pdf]] -- "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" -- Klemm, Li, and Hernandez 2000 <br />
<br />
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.<br />
<br />
Here is an article that looks more directly at visual evoked potential measures:<br />
<br />
[[File:ERP_Stereoscopic.pdf]] -- "Evoked Responses to Distinct and Nebulous Stereoscopic Stimuli" -- Dunlop et al 1983<br />
<br />
<br />
(11 September 2013) more on analysis methods:<br />
<br />
http://slesinsky.org/brian/misc/eulers_identity.html<br />
<br />
http://www.dspguide.com/ch8/1.htm<br />
<br />
[[File:Fftw3.pdf]]<br />
<br />
[[File:ParametricEEGAnalysis.pdf]]<br />
<br />
[[File:ICATutorial.pdf]]<br />
<br />
[[File:ICAFrequencyDomainEEG.pdf]]<br />
<br />
<br />
(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!<br />
<br />
Would be good to identify any papers suitable for more in-depth study. Currently have a wide field to graze for selections:<br />
<br />
[[File:DWTandFFTforEEG.pdf]] "EEG Classifier using Fourier Transform and Wavelet Transform" -- Maan Shaker, 2007<br />
<br />
[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999<br />
<br />
[[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''<br />
<br />
[[File:ContinuousAndDiscreteWaveletTransforms.pdf]] -- review of (pre-1990) wavelet literature -- Christopher Heil and David Walnut, 1989<br />
<br />
[[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<br />
<br />
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)<br />
<br />
----<br />
<br />
[http://www.nickgillian.com/software/grt OpenSource Machine Learning Algs from NG @MIT]<br />
<br>[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]<br />
<br>(discussed at meetup Wednesday 31 July 2013)<br />
<br>"Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" Klemm, Li, and Hernandez 2000 <br />
<br>[[File:CoherentEEGAmbiguousFigureBinding.pdf]]<br />
<br>"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."<br />
<br><br />
<br />
----<br />
<br />
[http://dreamsessions.net art, dream, and eeg]<br />
<br />
----<br />
<br />
[http://www.believermag.com/issues/200710/?read=article_aviv mind v brain, hobson v solms]<br />
<br>http://en.wikipedia.org/wiki/Activation-synthesis_hypothesis<br />
<br>[[File:HobsomREMDreamProtoconsciousness.pdf|Hobson09ProtosconsciousnessREMDream]]<br />
<br />
"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."<br />
<br />
----<br />
<br />
Berkeley Labs<br />
<br />
[http://gallantlab.org/index.html Gallant Group]<br />
<br>[http://walkerlab.berkeley.edu/ Walker Group]<br />
<br>[http://socrates.berkeley.edu/~plab/ Palmer Group]<br />
<br />
==Sleep Research==<br />
<br />
[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2335403/ Comment on the AASM Manual for the Scoring of Sleep and Associated Events]<br />
<br />
==random tangents==<br />
(following previous discussion) - we might select a few to study in more depth<br />
(... or not! Plenty more to explore - suggestions (random or otherwise) are welcome.<br />
<br />
'''stereoscopic perception:'''<br />
*[[File:ERP_Stereoscopic.pdf]] <br />
<br />
<br />
some (maybe) interesting background on Information Theory (cool title...)<br />
Claude Shannon: "Communication in the Presence of Noise"<br />
[[File:Shannon_noise.pdf]]<br />
"We will call a system that transmits without errors at the rate ''C'' an ideal system.<br />
Such a system cannot be achieved with any finite encoding process<br />
but can be approximated as closely as desired."<br />
<br />
wikipedia etc quick reads:<br />
https://en.wikipedia.org/wiki/Eeg<br />
https://en.wikipedia.org/wiki/Neural_synchronization<br />
https://en.wikipedia.org/wiki/Event-related_potentials<br />
http://www.scholarpedia.org/article/Spike-and-wave_oscillations<br />
http://www.scholarpedia.org/article/Thalamocortical_oscillations<br />
<br />
==Previously==<br />
<br />
[http://www.psychiclab.net/ Masahiro's EEG Device/IBVA Software]<br />
<br />
[http://www.instructables.com/id/open-brain-wave-interface-hardware-1/ and ... open source hardware design and kits on instructables.com]<br />
<br />
[http://brainstorms.puzzlebox.info/ Puzzlebox - Opensource BCI Developers]<br />
<br />
Morgan from GazzLab @ MissionBay/UCSF<br />
<br />
<br />
https://github.com/gazzlab<br />
<br />
Let's ease into a lightweight "journal club" discussion with this technical report from NeuroSky.<br />
<br />
Name: A user-friendly SSVEP-based brain-computer interface using a time-domain classifier, Luo A and Sullivan TJ 2010<br />
<br />
URL: [[File:NeuroSkyVEP.pdf]]<br />
<br />
Please add your comments & questions here.<br />
<br />
==Background Reading==<br />
<br />
http://nanosouffle.net/ (view into Arxiv.org)<br />
<br />
Name: Hunting for Meaning after Midnight, Miller 2007<br />
<br />
URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0002.pdf><br />
<br />
Name: Broken mirrors, Ram, VS, & Oberman, LM, 2006, Nov<br />
<br />
URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0003.pdf><br />
<br />
Ramachandran Critique<br />
<br />
http://blogs.scientificamerican.com/guest-blog/2012/11/06/whats-so-special-about-mirror-neurons/<br />
<br />
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773693/<br />
<br />
Sleep/Dream Studies<br />
<br />
http://www.cns.atr.jp/dni/en/publications/<br />
<br />
==NeuroSky Docs==<br />
[[File:NeuroSkyDongleProtocol.pdf]]<br />
<br />
[[File:NeuroSkyCommunicationsProtocol.pdf]]</div>104.11.211.148