(note wiki contains some useful clues re previous neuro research at Noisebridge ... For example, the Analog_EEG_Amp page describes some project ideas and work done by others here in 2012)
https://metacademy.org/ -- machine learning knowledge graph
http://www.thetalkingmachines.com/ -- podcast
https://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"
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://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"
http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf (works through simple example)
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://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"
 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://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
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 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"
 Hopfield nets and RBMs
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://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"
 (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:
(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
"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."
"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. This was perceived as the activation-synthesis model, stating that brain activation during REM sleep results in synthesis of dream creation. Hobson's five cardinal characteristics include: intense emotions, illogical content, apparent sensory impressions, uncritical acceptance of dream events, and difficulty in being remembered."
 Sleep Research
 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.
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
Morgan from GazzLab @ MissionBay/UCSF
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
Please add your comments & questions here.
 Background Reading
http://nanosouffle.net/ (view into Arxiv.org)
Name: Hunting for Meaning after Midnight, Miller 2007
Name: Broken mirrors, Ram, VS, & Oberman, LM, 2006, Nov