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if anyone's interested, there is a nice writeup describing the [[Analog_EEG_Amp]] 
'''This is essentially the groups meeting notes – a trail of bread crumbs of topics of conversation and projects entertained by the group'''


==Current Discussion==
note: Learn more about previous neuro research at Noisebridge on the wiki... For example, the [[Analog_EEG_Amp]] page describes some project ideas and work done by others here in 2012


(discussed at meetup Wednesday 31 July 2013)
==Websites and events that have piqued our interest==
<br>
 
http://cs375.stanford.edu/ -- Dan Yamins Large-Scale Neural Network Models for Neuroscience CS375
 
https://faculty.washington.edu/chudler/facts.html -- brain facts
 
http://onlinehub.stanford.edu/cs224 -- Natural Language Processing with Deep Learning
 
http://neurable.com/
 
https://noisebridge.net/wiki/NBDSM -- noiseBridge Deepnet and Statistical Mechanics -- first meetup @ noisebridge 7/6/17 at 7PM
 
https://metacademy.org/
-- machine learning knowledge graph
 
https://machinelearningguide.libsyn.com/rss -- machine learning guide podcast
 
http://www.thetalkingmachines.com/ -- podcast
 
https://karpathy.github.io/2015/05/21/rnn-effectiveness/
 
http://alexandre.barachant.org/papers/
 
http://ncs.ethz.ch/publications -- neuromorphic cognitive systems
 
https://github.com/crillab/gophersat/blob/master/examples/sat-for-noobs.md -- SAT solvers
 
https://media.ccc.de/v/34c3-8948-low_cost_non-invasive_biomedical_imaging -- Open EIT 34c3 talk https://github.com/OpenEIT
 
http://acrovirt.org/ -- sensors
 
http://www.neuroeducate.com/ -- citizen neuroscience
 
https://www.youtube.com/watch?v=9mZuyUzyN4Q -- "Categories for the Working Hacker"
 
http://radicalsciencenews.org/599-2/ -- "Deep Learning Fuels Nvidia’s Self-Driving Car Technology"
 
https://arxiv.org/abs/1803.03635 -- "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" Jonathan Frankle, Michael Carbin
 
== Global Workspace Theory ==
 
http://bernardbaars.pbworks.com/f/BaarsJCS1997.pdf -- "IN THE THEATRE OF CONSCIOUSNESS"
 
 
== Symbolic Mathematics ==
 
https://arxiv.org/pdf/1912.01412.pdf -- "Deep Learning for Symbolic Mathematics"
 
https://www.scottaaronson.com/busybeaver.pdf -- "A Relatively Small Turing Machine Whose Behavior Is Independent of Set Theory"
 
https://www.scottaaronson.com/blog/?p=2725 -- "The 8000th Busy Beaver number eludes ZF set theory: new paper by Adam Yedidia and me"
 
== Vision ==
 
https://webvision.med.utah.edu/
 
== Language Models ==
 
https://blog.scaleway.com/2019/building-a-machine-reading-comprehension-system-using-the-latest-advances-in-deep-learning-for-nlp/ -- "Natural Language Processing: the age of Transformers"
 
https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf -- "Language Models are Unsupervised Multitask Learners"
 
https://arxiv.org/pdf/1706.03762 -- "Attention Is All You Need"
 
https://arxiv.org/pdf/1705.03122.pdf -- "Convolutional Sequence to Sequence Learning"
 
https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf -- "Sequence to Sequence Learning with Neural Networks"
 
https://arxiv.org/pdf/1508.07909.pdf -- "Neural Machine Translation of Rare Words with Subword Units"
 
https://github.com/rsennrich/subword-nmt
 
https://github.com/rowanz/grover - grover GPU/TPU based GPT-2 transformer implementation
 
https://arxiv.org/pdf/1905.12616.pdf -- "Defending Against Neural Fake News"
 
https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html - blog post on attention
 
https://github.com/lilianweng/transformer-tensorflow - sample implementation of "Attention Is All You Need"
 
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py - "official" implementation of "Attention Is All You Need"
 
https://jalammar.github.io/illustrated-gpt2/ - The Illustrated GPT-2 (Visualizing Transformer Language Models)
 
== Bioengineering ==
 
https://nips.cc/Conferences/2018/Schedule?showEvent=12487 -- "What Bodies Think About: Bioelectric Computation Outside the Nervous System, Primitive Cognition, and Synthetic Morphology"
 
== Data Visualization ==
 
https://www.csc2.ncsu.edu/faculty/healey/download/tvcg.12b.pdf -- "Interest Driven Navigation in Visualization"
 
== Fractal Dementia ==
 
https://pdfs.semanticscholar.org/0018/7c742e60d35d5034a63251e31e1b8d96c70b.pdf -- "Comparison of Fractal Dimension Algorithms for the Computation of Eeg Biomarkers for Dementia"
 
== Brain Activity Dynamics ==
 
https://arxiv.org/pdf/1802.02523.pdf -- "Plasma Brain Dynamics (PBD): a Mechanism for EEG Waves Under Human Consciousness"
 
https://arxiv.org/pdf/1206.1108.pdf -- "Thermodynamic Model of Criticality in the Cortex Based On EEG/ECOG Data"
 
https://www.bm-science.com/images/bms/publ/art63.pdf -- "Topographic Mapping of Rapid Transitions in EEG Multiple Frequencies"
 
== Silent Speech ==
 
https://dam-prod.media.mit.edu/x/2018/03/23/p43-kapur_BRjFwE6.pdf -- "AlterEgo: A Personalized Wearable Silent Speech Interface"
 
== Image Reconstruction ==
 
https://www.biorxiv.org/content/biorxiv/early/2017/12/28/240317.full.pdf -- "Deep image reconstruction from human brain
activity"
 
== EEG Electrodes ==
 
https://sites.google.com/site/biofeedbackpages/velcro-sensors -- Saline electrodes
 
https://www.commsp.ee.ic.ac.uk/~mandic/Ear_EEG_IEEE_Pulse_2012.pdf -- "The In-the-Ear
Recording Concept"
 
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8357918 -- "Dry-Contact Electrode Ear-EEG"
 
== Generative Adversarial Networks (GAN) ==
 
https://arxiv.org/pdf/1710.08864 -- "One pixel attack for fooling deep neural networks"
 
== Kolmolgorov Complexity ==
 
ftp://ftp.idsia.ch/pub/juergen/loconet.pdf -- "Discovering Neural Nets with Low Kolmolgorov Complexity and High Generalization Capability"
 
https://papers.nips.cc/paper/394-chaitin-kolmogorov-complexity-and-generalization-in-neural-networks.pdf -- "Chaitin-Kolmogorov Complexity and Generalization in Neural Networks"
 
== OpenCV ==
 
http://arnab.org/blog/so-i-suck-24-automating-card-games-using-opencv-and-python
 
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.585&rep=rep1&type=pdf -- "Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm"
 
== Category Theory ==
 
https://arxiv.org/pdf/1711.10455 -- "Backprop as Functor: A compositional perspective on supervised learning"
 
http://math.ucr.edu/home/baez/rosetta.pdf -- "Physics, Topology, Logic and Computation: A Rosetta Stone"
 
https://www.youtube.com/watch?v=BF6kHD1DAeU -- "Category theory foundations 1.0 — Steve Awodey"
 
== Proof Searcher ==
 
https://arxiv.org/pdf/cs/0207097 -- "Optimal Ordered Problem Solver"
 
http://people.idsia.ch/~juergen/ultimatecognition.pdf -- "Ultimate Cognition a la Gödel"
 
http://people.idsia.ch/~juergen/selfreflection.pdf -- "Towards an Actual Gödel Machine Implementation"
 
== Capsule Models ==
 
https://arxiv.org/pdf/1710.09829.pdf -- "Dynamic Routing Between Capsules"
 
https://openreview.net/pdf?id=HJWLfGWRb -- "Matrix Capsules with EM Routing"
 
== Multivariate Coherence Training ==
 
https://www.youtube.com/watch?v=qGYjvLki0WY
== Infrared Neuroimaging ==
 
http://www.ecse.rpi.edu/~yazici/bio_book.pdf -- "Near Infrared Imaging and Spectroscopy for Brain Activity Monitoring"
 
http://fangyenlab.seas.upenn.edu/pubs/isr.pdf -- "Intrinsic optical signals in neural tissues:
measurements, mechanisms, and applications"
 
== Geometry ==
 
http://arxiv.org/abs/1710.10784 -- "How deep learning works --The geometry of deep learning"
 
== Affective Computing ==
 
http://affect.media.mit.edu/pdfs/05.ahn-picard-acii.pdf -- "Affective-Cognitive Learning and Decision
Making: A Motivational Reward Framework For Affective Agents"
 
== Explainability ==
 
http://arxiv.org/abs/1708.01785 -- "Interpreting CNN knowledge via an Explanatory Graph"
 
== NLP ==
 
https://arxiv.org/pdf/1605.06640 -- "Programming with a Differentiable Forth Interpreter"
 
https://pdfs.semanticscholar.org/f683/dbe8a22d633ad3a2cff379b055b26684a838.pdf -- "Solving General Arithmetic Word Problems"
 
https://arxiv.org/pdf/1611.04558.pdf -- "Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation"
 
http://emnlp2014.org/papers/pdf/EMNLP2014162.pdf -- "GloVe: Global Vectors for Word Representation"
 
== RNNs ==
 
https://arxiv.org/pdf/1611.01576.pdf -- "Quasi Recurrent Neural Networks"
 
== Hyper-parameter Optimization ==
 
https://arxiv.org/abs/1603.06560 -- "Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization"
 
== Transfer Learning ==
 
http://arxiv.org/abs/1710.10776v1 -- "Transfer Learning to Learn with Multitask Neural Model Search"
 
== Reinforcement Learning ==
 
http://www2.hawaii.edu/~sstill/StillPrecup2011.pdf -- "An information-theoretic approach to curiosity-driven reinforcement learning"
 
https://arxiv.org/abs/1605.06676 -- "Learning to Communicate with Deep Multi-Agent Reinforcement Learning"
 
== Learning to Learn ==
 
https://arxiv.org/pdf/1703.01041.pdf -- "Large-Scale Evolution of Image Classifiers"
 
https://arxiv.org/pdf/1611.01578 -- "Neural Architecture Search with Reinforcement Learning"
 
== The Utility of "Noise" in ML ==
 
https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf -- "Dropout:  A Simple Way to Prevent Neural Networks from Overfitting"
 
http://yann.lecun.com/exdb/publis/pdf/lecun-90b.pdf -- "Optimal Brain Damage"
 
https://arxiv.org/pdf/1502.01852.pdf -- "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"
 
== One-shot learning ==
 
https://arxiv.org/abs/1605%2E06065 -- "One-shot Learning with Memory-Augmented Neural Networks"
 
== Program Synthesis ==
 
https://pdfs.semanticscholar.org/0163/35ce7e0a073623e1deac7138b28913dbf594.pdf -- "Human-level concept learning through probabilistic program induction"
 
https://arxiv.org/pdf/1511.06279.pdf -- "Neural Programmer: Inducing Latent Programs with Gradient Descent"
 
https://arxiv.org/abs/1608.04428 -- "TerpreT: A Probabilistic Programming Language for Program Induction" Gaunt et al 2016
 
== Machine Learning Interaction ==
 
https://teachablemachine.withgoogle.com/#
 
== Game Theory ==
 
https://arxiv.org/abs/1707.01068v1 -  Maintaining cooperation in complex social dilemmas using deep reinforcement learning
 
== Questions of Physics and Free Will ==
 
http://www.scottaaronson.com/papers/giqtm3.pdf - The Ghost in the Quantum Turing Machine
 
== CNN ==
 
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/ - "A Beginner's Guide To Understanding Convolutional Neural Networks"
 
https://adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/ - "A Beginner's Guide To Understanding Convolutional Neural Networks Part 2"
 
http://scs.ryerson.ca/~aharley/vis/harley_vis_isvc15.pdf -- "An Interactive Node-Link Visualization
of Convolutional Neural Networks"
 
http://white.stanford.edu/teach/index.php/An_Introduction_to_Convolutional_Neural_Networks
 
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf -- "Learning to Generate Chairs With Convolutional Neural Networks"
 
http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf
-- "What's Wrong With Deep Learning?"
 
== Mind-Body Relations ==
 
http://www.pnas.org/content/111/20/7379.full.pdf -- "Voluntary activation of the sympathetic nervous system and attenuation of the innate immune response in humans"
 
== Math ==
 
https://arxiv.org/pdf/1311.1090.pdf -- "Polyhedrons and Perceptrons Are Functionally Equivalent"
 
Example code and training data using polyhedrons developed by author of above paper:  https://www.noisebridge.net/wiki/DreamTeam#Code
 
== Bayesian Inference ==
 
https://noisebridge.net/images/e/ef/Perception_is_in_the_Details12.pdf --
"Perception is in the Details: A Predictive Coding Account of the Psychedelic Phenomenon"
 
http://rsif.royalsocietypublishing.org/content/10/86/20130475 --
"Life as we know it"
 
http://jmlr.csail.mit.edu/proceedings/papers/v31/wang13b.pdf --
"Collapsed Variational Bayesian Inference for Hidden Markov Models"
 
http://www.datalab.uci.edu/papers/nips06_cvb.pdf --
"A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation"
 
http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf --
"Hierarchical Bayesian inference in the visual cortex"
 
https://www.researchgate.net/profile/Til_Bergmann/publication/262423308_Temporal_coding_organized_by_coupled_alpha_and_gamma_oscillations_prioritize_visual_processing/links/0deec537d1bfda474c000000/Temporal-coding-organized-by-coupled-alpha-and-gamma-oscillations-prioritize-visual-processing.pdf --
"Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing"
 
http://www.cell.com/neuron/pdf/S0896-6273(15)00823-5.pdf --
"Rhythms for Cognition: Communication through Coherence"
 
http://www.biorxiv.org/content/biorxiv/early/2014/05/06/004804.full.pdf --
"Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels"
 
== Speech Recognition ==
 
https://arxiv.org/pdf/1612.00694v1 -- "ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA"
 
== Sound Classification ==
 
https://arxiv.org/pdf/1608.04363v2 -- "Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification"
 
https://arxiv.org/pdf/1605.09507 "Deep convolutional neural networks for predominant instrument recognition in polyphonic music"
 
== Hardware Implementations - FPGA, GPU, etc ==
 
https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf --
"Artificial neural networks in hardware: A survey of two decades of progress"
 
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.133.9185&rep=rep1&type=pdf
"A Self-Repairing Multiplexer-Based FPGA Inspired by Biological Processes"
 
http://www.genetic-programming.com/jkpdf/fpga1998.pdf -- "Evolving Computer Programs using Rapidly Reconfigurable Field-Programmable Gate Arrays and Genetic Programming"
 
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.2588&rep=rep1&type=pdf -- "Flexible Implementation of Genetic Algorithms on FPGAs"
 
http://www.users.muohio.edu/jamiespa/html_papers/gem_10.pdf -- "Revisiting Genetic Algorithms for the FPGA Placement Problem"
 
https://arxiv.org/pdf/1609.09296v1 -- "Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs"
 
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.7533&rep=rep1&type=pdf -- "FPGA-TARGETED NEURAL ARCHITECTURE FOR EMBEDDED ALERTNESS DETECTION"
 
https://arxiv.org/pdf/1611.02450v1 -- "PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks"
 
https://arxiv.org/pdf/1605.06402v1 -- "Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks"
 
https://homes.cs.washington.edu/~luisceze/publications/snnap-hpca-2015.pdf -- "SNNAP: Approximate Computing on Programmable SoCs via Neural Acceleration"
 
https://arxiv.org/pdf/1701.00485v2 -- "Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices"
 
== VLSI ==
 
http://ncs.ethz.ch/pubs/pdf/Indiveri_etal06.pdf -- "A VLSI array of low-power spiking neurons and bistable synapses with spike–timing dependent plasticity"
 
== Pruning ==
 
http://papers.nips.cc/paper/5784-learning-both-weights-and-connections-for-efficient-neural-network.pdf --
"Learning both Weights and Connections for Efficient Neural Networks"
 
https://arxiv.org/pdf/1701.04465 -- "The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning"
 
https://arxiv.org/pdf/1512.08571 -- "Structured Pruning of Deep Convolutional Neural Networks"
 
https://arxiv.org/pdf/1611.01427 -- "Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks"
 
== Efficient Neural Networks via Compression, Quantization, Model Reduction, etc ==
 
https://arxiv.org/pdf/1504.04788 -- "Compressing Neural Networks with the Hashing Trick"
 
https://arxiv.org/pdf/1509.08745 -- "Compression of Deep Neural Networks on the Fly"
 
https://arxiv.org/pdf/1502.03436 -- "An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections"
 
https://arxiv.org/pdf/1510.00149 -- "Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding"
 
https://arxiv.org/pdf/1612.00891 -- "Parameter Compression of Recurrent Neural Networks and Degradation of Short-term Memory"
 
https://arxiv.org/pdf/1609.07061 -- "Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations"
 
https://arxiv.org/pdf/1607.05418 -- "Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off"
 
https://arxiv.org/pdf/1602.08194 -- "Scalable and Sustainable Deep Learning via Randomized Hashing"
 
https://arxiv.org/pdf/1508.05463 -- "StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity"
 
https://arxiv.org/pdf/1412.7024 -- "Training Deep Neural Networks with Low Precision Multiplications"
 
https://arxiv.org/pdf/1612.03940 -- "Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks"
 
https://arxiv.org/pdf/1609.00222 -- "Ternary Neural Networks for Resource-Efficient AI Applications"
 
== Neural Network Hyperparameter Optimization ==
 
https://arxiv.org/pdf/1601.00917 -- "DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks"
 
== Neural Network based EEG Analysis ==
end
http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"
 
http://inter-eng.upm.ro/2012/files/proceedings/papers/paper72.pdf --
"Neural Network Parallelization on FPGA Platform for EEG Signal Classification"
 
== Seizure Detection ==
 
also see https://noisebridge.net/wiki/Kaggle for a (September 2016) current project!
 
and https://github.com/kevinjos/kaggle-aes-seizure-prediction (some earlier exploration, November 2014)
 
(broken link, sorry) http://www.sersc.org/journals/ijsip/vol7_no5/26.pdf --
"A Neural Network Model for Predicting Epileptic Seizures based on Fourier-Bessel Functions"
 
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf --
"A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"
 
(another broken link) http://cs.uni-muenster.de/Professoren/Lippe/diplomarbeiten/html/eisenbach/Untersuchte%20Artikel/PPHD+00.pdf --
"Recurrent neural network based preenddiction of epileptic seizures in intra- and extracranial EEG"
 
== Visible Light Sensor Network ==
 
http://infoteh.etf.unssa.rs.ba/zbornik/2016/radovi/KST-1/KST-1-15.pdf --
"Analysis of Visible Light Communication System for Implementation in Sensor Networks"
 
== Neurophysiology ==
 
http://www.buzsakilab.com/content/PDFs/BuzsakiKoch2012.pdf -- "The origin of extracellular fields and
currents — EEG, ECoG, LFP and spikes"
 
== Signal Processing ==
 
http://www.ti.com/lit/an/slyt438/slyt438.pdf -- "How delta-sigma ADCs work, Part 2"
 
http://provideyourown.com/2011/analogwrite-convert-pwm-to-voltage/ -- "Arduino’s AnalogWrite – Converting PWM to a Voltage"
 
http://sim.okawa-denshi.jp/en/PWMtool.php -- "RC Low-pass Filter Design for PWM (Transient Analysis Calculator)"
 
== Hyperdimensional Computing ==
 
http://redwood.berkeley.edu/pkanerva/papers/kanerva09-hyperdimensional.pdf --
"Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors"
 
http://arxiv.org/pdf/1602.03032.pdf --
"Associative Long Short-Term Memory"
 
== Bird Flocks and Maximum Entropy ==
 
http://arxiv.org/pdf/1107.0604v1 --
"Statistical Mechanics and Flocks of Birds"
 
https://arxiv.org/pdf/1311.2319.pdf -- "Statistical Mechanics of Surjective Cellular Automata"
 
https://pdfs.semanticscholar.org/fa84/5d15a54e99519d83a3ae1510200dc2eca471.pdf -- "Inhomogeneous Cellular Automata and Statistical Mechanics"
 
http://arxiv.org/pdf/1307.5563v1 --
"Social interactions dominate speed control in driving natural flocks toward criticality"
 
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_penfield.pdf --
"Information and Entropy (Course Notes)"
 
== Whale Songs ==
 
https://arxiv.org/pdf/1307.0589.pdf -- "The Orchive : Data mining a massive bioacoustic archive"
 
https://www.researchgate.net/profile/Herbert_Roitblat/publication/13429327_The_neural_network_classification_of_false_killer_whale_%28Pseudorca_crassidens%29_vocalizations/links/540d2ff60cf2df04e75478cd.pdf -- "The neural network classification of false killer whale (Pseudorca crassidens) vocalizations"
 
http://users.iit.demokritos.gr/~paliourg/papers/PhD.pdf -- "REFINEMENT OF TEMPORAL CONSTRAINTS IN AN EVENT RECOGNITION SYSTEM USING SMALL DATASETS"
 
https://www.nersc.no/sites/www.nersc.no/files/master_thesis_sebastian_menze.pdf -- "Estimating fin whale distribution from ambient noise spectra using Bayesian inversion"
 
http://sis.univ-tln.fr/~glotin/IJCNN2015_IHMMbioac_BartChamGlot.pdf -- "Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis"
 
https://www.inf.ed.ac.uk/publications/thesis/online/IM030057.pdf -- "Hidden Markov Model Clustering of Acoustic Data"
 
http://danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/ -- Using deep learning to listen for whales
 
== Computational Cognitive Neuroscience ==
 
http://www.pnas.org/content/110/41/16390.full -- "Indirection and symbol-like processing in the prefrontal cortex and basal ganglia"
 
http://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/docs/jaf.pdf -- "Connectionism and Cognitive Architecture: A Critical Analysis"
 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2728678/pdf/nihms131814.pdf -- Neves et al 2008 "Cell Shape and Negative Links in Regulatory Motifs Together Control Spatial Information Flow in Signaling Networks"
 
http://psych.colorado.edu/~oreilly/papers/AisaMingusOReilly08.pdf -- "The Emergent Neural Modeling System"
 
https://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators
 
== Text Generation ==
 
http://www.cs.toronto.edu/~ilya/pubs/2011/LANG-RNN.pdf -- "Generating Text with Recurrent Neural Networks"
 
== Games ==
 
http://setgame.com/sites/default/files/teacherscorner/COGNITIVE%20MODELING%20WITH%20SET.pdf -- "How to Construct a Believable Opponent using Cognitive Modeling in the Game of Set"
 
http://www-personal.umich.edu/~charchan/SET.pdf -- "SETs and Anti-SETs: The Math Behind the Game of SET"
 
http://personal.plattsburgh.edu/quenelgt/talks/set.pdf -- "Introduction to Set"
 
http://web.engr.illinois.edu/~pbg/papers/set.pdf -- "On the Complexity of the Game of Set"
 
http://www.warwick.ac.uk/staff/D.Maclagan/papers/set.pdf -- "The Card Game Set"
 
http://www.math.ucdavis.edu/~anne/FQ2011/set_game.pdf -- "The Game Set"
 
https://www.youtube.com/watch?v=k2rgzZ2WXKo -- "Best Practices for Procedural Narrative Generation" Chris Martens
 
== 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)
 
https://github.com/Micah1/neurotech (brainduino code)
 
== Hidden Markov Models ==
 
http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf
-- "A Revealing Introduction to Hidden Markov Models"
 
http://www.jelmerborst.nl/pubs/Borst2013b.pdf
-- "Discovering Processing Stages by combining EEG with Hidden Markov Models"
 
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf
-- "A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"
 
http://www.schalklab.org/sites/default/files/misc/Coupled%20hidden%20markov%20model%20for%20electrocorticographic%20signal%20classification.pdf
-- "Coupled Hidden Markov Model for Electrocorticographic Signal Classification"
 
== Long Short Term Memory ==
 
http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
-- "Long Short-Term Memory"
 
ftp://ftp.idsia.ch/pub/juergen/2002_icannMusic.pdf
-- "Learning The Long-Term Structure of the Blues"
 
http://www.overcomplete.net/papers/nn2012.pdf
-- "A generalized LSTM-like training algorithm for second-order recurrent neural networks"
 
http://www.iro.umontreal.ca/~eckdoug/papers/2003_nn.pdf
-- "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets"
 
http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html
-- "Long Short-Term Memory dramatically improves Google Voice etc"
 
https://arxiv.org/pdf/1511.05552v4.pdf --
"Recurrent Neural Networks Hardware Implementation on FPGA"
 
http://vast.cs.ucla.edu/sites/default/files/publications/ASP-DAC2017-1352-11.pdf --
"FPGA-based Accelerator for Long Short-Term Memory Recurrent Neural Networks"
 
== Question Answering ==
 
http://www.overcomplete.net/papers/bica2012.pdf
-- "Neural Architectures for Learning to Answer Questions"
 
https://cs.umd.edu/~miyyer/pubs/2014_qb_rnn.pdf
-- "A Neural Network for Factoid Question Answering over Paragraphs"
 
http://arxiv.org/pdf/1502.05698.pdf
-- "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"
 
http://www.peterschueller.com/pub/2014/2014_winograd_schemas_relevance_knowledge_graphs.pdf
-- "Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs"
 
http://ijcai.org/papers15/Papers/IJCAI15-190.pdf
-- "Towards Addressing the Winograd Schema Challenge — Building and Using a Semantic Parser and a Knowledge Hunting Module"
 
http://arxiv.org/pdf/1506.05869v2.pdf
-- "A Neural Conversational Model"
 
http://arxiv.org/pdf/1508.05508v1.pdf
-- "Towards Neural Network-based Reasoning"
 
http://www.visualqa.org/vqa_iccv2015.pdf
-- "VQA: Visual Question Answering"
 
== Propagators ==
Cells must support three operations:
*add some content
*collect the content currently accumulated
*register a propagator to be notified when the accumulated content changes
*When new content is added to a cell, the cell must merge the addition with the content already present. When a propagator asks for the content of a cell, the cell must deliver a complete summary of the information that has been added to it.
*The merging of content must be commutative, associative, and idempotent. The behavior of propagators must be monotonic with respect to the lattice induced by the merge operation.
*http://web.mit.edu/~axch/www/art.pdf *http://groups.csail.mit.edu/mac/users/gjs/propagators/
*http://dustycloud.org/blog/sussman-on-ai/
 
== Boosting ==
 
http://www.cs.princeton.edu/courses/archive/spr08/cos424/readings/Schapire2003.pdf
-- "The Boosting Approach to Machine Learning An Overview"
 
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.9955&rep=rep1&type=pdf
-- "Ensembling Neural Networks: Many Could Be Better Than All"
 
http://www.cs.columbia.edu/~rocco/Public/mlj9.pdf
-- "Random Classification Noise Defeats All Convex Potential Boosters"
 
== Support Vector Machines ==
 
http://research.microsoft.com/en-us/um/people/cburges/papers/svmtutorial.pdf
-- "A Tutorial on Support Vector Machines for Pattern Recognition"
 
== Wire Length / Small World Networks ==
 
http://koulakovlab.cshl.edu/publications/koulakov_chklovskii_2000.pdf
-- "A wire length minimization approach to ocular dominance patterns in mammalian visual cortex"
 
http://papers.nips.cc/paper/1910-foundations-for-a-circuit-complexity-theory-of-sensory-processing.pdf
-- "Foundations for a Circuit Complexity Theory of Sensory Processing"
 
https://www.nada.kth.se/~cjo/documents/small_world.pdf
-- "Small-World Connectivity and Attractor Neural Networks"
 
http://www.doc.ic.ac.uk/~mpsha/ShanahanPhysRevEPreprint.pdf
-- "The Dynamical Complexity of Small-World Networks of Spiking Neurons"
 
http://www.dam.brown.edu/people/elie/papers/small_world.pdf
-- "Autoassociative Memory Retrieval and Spontaneous Activity Bumps in Small-World Networks of Integrate-and-Fire Neurons"
 
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.4501&rep=rep1&type=pdf
-- "Transition from Random to Small-World Neural Networks by STDP Learning Rule"
 
http://tamar.tau.ac.il/~eshel/papers/J_Neural_Engineering.pdf
-- "Compact self-wiring in cultured neural networks"
 
== Backpropagation ==
 
http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf -- "Neural Networks - A Systematic Introduction"
 
http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf (works through simple example)
 
http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
 
http://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf
 
also see http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf (entire book "Neural Networks - a Systemic Introduction" by Raul Rojas)
 
http://work.caltech.edu/lectures.html Hoeffding's inequality, VC Dimension and Back Propagation ANN
 
http://www.cs.stir.ac.uk/research/publications/techreps/pdf/TR148.pdf ("Learning XOR: exploring the space of a classic problem")
 
http://www.sysc.pdx.edu/classes/Werbos-Backpropagation%20through%20time.pdf
-- "Backpropagation Through Time: What it Does and How to Do It"
 
== Computer Vision ==
 
http://www.cv-foundation.org/openaccess/CVPR2015.py -- some interesting papers here
 
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf -- "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"
 
== Visual Perception (Biological Systems) ==
 
http://cbcl.mit.edu/publications/ps/Serre_etal_PBR07.pdf
-- "A quantitative theory of immediate visual recognition"
 
http://www.dam.brown.edu/ptg/REPORTS/Invariance.pdf
-- "Invariance and Selectivity in the Ventral Visual Pathway"
 
http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf
-- "Hierarchical Bayesian inference in the visual cortex"
 
== Neural Synchrony ==
 
http://arxiv.org/pdf/1312.6115.pdf
-- "Neuronal Synchrony in Complex-Valued Deep Networks"
 
== Spiking Neural Networks ==
 
http://ncs.ethz.ch/projects/evospike/publications/ICONIP2011%20Springer%20LNCS%20Nutta.pdf -- "EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network"
 
http://dai.fmph.uniba.sk/~benus/confers/CNGM_ICANN05.pdf -- "Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques"
 
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3902 -- "Pattern Recognition in a Bucket"
 
http://www.igi.tugraz.at/maass/psfiles/221.pdf -- "Noise as a Resource for Computation and Learning in Spiking Neural Networks"
 
http://web.stanford.edu/group/brainsinsilicon/goals.html -- Neurogrid
 
http://apt.cs.manchester.ac.uk/projects/SpiNNaker/ -- SpiNNaker
 
http://personalpages.manchester.ac.uk/staff/javier.navaridas/pubs/2012.jpdc.pdf -- more on SpiNNaker
 
http://apt.cs.manchester.ac.uk/ftp/pub/apt/papers/JavN_ICS09.pdf -- "Understanding the Interconnection Network of SpiNNaker"
 
==Hierarchical Temporal Memory==
 
https://github.com/numenta/nupic/wiki/Hierarchical-Temporal-Memory-Theory
 
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000532 -- "Towards a Mathematical Theory of Cortical Micro-circuits"
 
==Distributed Neural Networks==
 
https://www.paypal-engineering.com/2015/01/12/deep-learning-on-hadoop-2-0-2/ -- paypal engineering blog, paper describes implementation of neural network as described by [http://www.maths.bris.ac.uk/~maxvd/reduce_dim.pdf Hinton 2006] on Hadoop
 
http://www.sciencemag.org/content/suppl/2006/08/04/313.5786.504.DC1/Hinton.SOM.pdf -- supplementary material for [http://www.maths.bris.ac.uk/~maxvd/reduce_dim.pdf Hinton 2006]
 
http://www.cs.otago.ac.nz/staffpriv/hzy/papers/pdcs03.pdf -- "Parallelization of a Backpropagation Neural Network on a Cluster Computer"
 
http://arxiv.org/pdf/1404.5997v2.pdf -- "One weird trick for parallelizing convolutional neural networks"
 
http://www.ics.uci.edu/~jmoorkan/pub/gpusnn-ijcnn.pdf -- "Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors"
 
==Mixture of Experts==
 
http://www.cs.toronto.edu/~fritz/absps/jjnh91.pdf -- "Adaptive Mixtures of Local Experts"
 
==Hopfield nets and RBMs==
 
http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf -- Yoshua Bengio, 2009, Learning Deep Architectures for AI
 
http://deeplearning.cs.cmu.edu/ -- Syllabus for cs course on deep learning, possible source of literature for the library
 
https://class.coursera.org/neuralnets-2012-001/lecture -- Lecture 11 - 15 to aid in understanding of Hinton 2006 paper above
 
http://www.pnas.org/content/79/8/2554.full.pdf -- "Neural networks and physical systems with emergent collective computational abilities" (Hopfield 1982)
 
http://page.mi.fu-berlin.de/rojas/neural/chapter/K13.pdf -- "The Hopfield Model" (Rojas 1996)
 
http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"
 
http://www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/cameraready_xwjiabibe.pdf -- "A Novel Semi-supervised Deep Learning Framework
for Affective State Recognition on EEG Signals"
 
http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf -- "A Practical Guide to Training Restricted Boltzmann Machines"
 
http://arxiv.org/pdf/1503.07793v2.pdf
-- "Gibbs Sampling with Low-Power Spiking Digital Neurons"
 
http://arxiv.org/pdf/1311.0190v1 -- "On the typical properties of inverse problems in statistical mechanics" Iacopo Mastromatteo 2013
 
http://jmlr.org/proceedings/papers/v5/salakhutdinov09a/salakhutdinov09a.pdf -- "Deep Boltzmann Machines" Salakhutdinov & Hinton 2009
 
http://www.cs.toronto.edu/~hinton/absps/tr00-004.pdf -- "Training Products of Experts by Minimizing Contrastive Divergence"
 
http://www.eecg.toronto.edu/~pc/research/publications/ly.fpga2009.submitted.pdf -- "A High-Performance FPGA Architecture for Restricted
Boltzmann Machines" Ly & Chow 2009
 
https://pdfs.semanticscholar.org/85fa/f7c3c05388e2bcd097a416606bdd88fc0c7c.pdf -- "A MULTI-FPGA ARCHITECTURE FOR STOCHASTIC RESTRICTED BOLTZMANN MACHINES" Ly & Chow 2009
 
== Variational Renormalization ==
 
https://arxiv.org/pdf/1410.3831 -- "An exact mapping between the Variational Renormalization Group and Deep Learning"
 
== Neuromorphic Stuff ==
 
https://arxiv.org/pdf/1508.01008.pdf -- "INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks" Chung, Shin & Kang 2015
 
== Markov Chain Monte Carlo ==
 
http://www.cs.princeton.edu/courses/archive/spr06/cos598C/papers/AndrieuFreitasDoucetJordan2003.pdf
-- "An Introduction to MCMC for Machine Learning"
 
http://jmlr.org/proceedings/papers/v37/salimans15.pdf
-- "Markov Chain Monte Carlo and Variational Inference: Bridging the Gap"
 
https://www.umiacs.umd.edu/~resnik/pubs/LAMP-TR-153.pdf -- "Gibbs Sampling for the Uninitiated"
 
==Entrainment==
 
http://www.scirp.org/Journal/PaperDownload.aspx?paperID=33251 -- "Alpha Rhythms Response to 10 Hz Flicker Is Wavelength Dependent"
 
http://www.brainmachine.co.uk/wp-content/uploads/Herrmann_Flicker.pdf -- "EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena"
 
http://www.jneurosci.org/content/23/37/11621.full.pdf -- "Human Cerebral Activation during Steady-State Visual-Evoked Responses"
 
http://www.dauwels.com/Papers/CogDyn%202009.pdf -- "On the synchrony of steady state visual evoked potentials and oscillatory burst events"
 
https://www.tu-ilmenau.de/fileadmin/public/lorentz-force/publications/peer/2012/haueisen2012/Halbleib_JCN_2012_Topographic_analysis_photic_driving.pdf -- "Topographic Analysis of Engagement and Disengagement of Neural Oscillators in Photic Driving: A Combined Electroencephalogram/Magnetoencephalogram Study"
 
==Mining Scientific Literature==
 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC153503/pdf/1471-2105-4-11.pdf -- "PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine" Donaldson 2003 BMC Bioinformatics
 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674139/pdf/pcbi.1004630.pdf -- "Text Mining for Protein Docking" Badal 2015 PLoS Comput Biol.
 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4691339/pdf/bav116.pdf -- "Biocuration with insufficient resources and fixed timelines" Rodriguez-Esteban 2015 Database: The Journal of Biological Databases and Curation
 
==(not necessarilly very) Current Discussion==
 
re Tononi's "Integrated Information Theory" http://www.scottaaronson.com/blog/?p=1799
 
(19 February 2014) starting to think about possibility for experiments (loosely) related to [https://en.wikipedia.org/wiki/Visual_evoked_potential Visual Evoked Potential] research again - for instance:
 
http://www.biosemi.com/publications/pdf/Bierman.pdf -- Wave function collapse
 
[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999
 
[[File:CoherentEEGAmbiguousFigureBinding.pdf]] -- "Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" -- Klemm, Li, and Hernandez 2000
 
Note these two papers flog coherence measures - not trying to focus so much on that analysis right now, more interested in general understanding of what these experiments are about with possible goal of designing simpler experiments & analysis of similar perceptual/cognitive phenomena.
 
Here is an article that looks more directly at visual evoked potential measures:
 
[[File:ERP_Stereoscopic.pdf]] -- "Evoked Responses to Distinct and Nebulous Stereoscopic Stimuli" -- Dunlop et al 1983
 
 
(11 September 2013) more on analysis methods:
 
http://slesinsky.org/brian/misc/eulers_identity.html
 
http://www.dspguide.com/ch8/1.htm
 
[[File:Fftw3.pdf]]
 
[[File:ParametricEEGAnalysis.pdf]]
 
[[File:ICATutorial.pdf]]
 
[[File:ICAFrequencyDomainEEG.pdf]]
 
 
(21 August 2013) - readings relating statistical (etc math / signal processing / pattern recognition / machine learning) methods for EEG data interpretation.  A lot of stuff, a bit of nonsense ... and ... statistics!
 
Would be good to identify any papers suitable for more in-depth study.  Currently have a wide field to graze for selections:
 
[[File:DWTandFFTforEEG.pdf]] "EEG Classifier using Fourier Transform and Wavelet Transform" -- Maan Shaker, 2007
 
[[File:Schak99InstantaneousCoherenceStroopTask.pdf]] -- "Instantaneous EEG Coherence Analysis During the Stroop Task" -- Schack et al 1999
 
[[File:KulaichevCoherence.pdf]] -- "The Informativeness of Coherence Analysis in EEG Studies" -- A. P. Kulaichev 2009 ''note: interesting critical perspective re limitations, discussion of alternative analytics''
 
[[File:ContinuousAndDiscreteWaveletTransforms.pdf]] -- review of (pre-1990) wavelet literature -- Christopher Heil and David Walnut, 1989
 
[[File:EEGGammaMeditation.pdf]] -- "Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self" -- Dietrich Lehman et al 2001
 
http://neuro.hut.fi/~pavan/home/Hyvarinen2010_FourierICA_Neuroimage.pdf - "Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis" -- Aapo Hyvarinen, Pavan Ramkumar, Lauri Parkkonen, Riitta Hari - paper published in Neuroimage vol 49 (2010)
 
----
 
[http://www.nickgillian.com/software/grt OpenSource Machine Learning Algs from NG @MIT]
<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>(discussed at meetup Wednesday 31 July 2013)
<br>"Coherent EEG Indicators of Cognitive Binding During Ambiguous Figure Tasks" Klemm, Li, and Hernandez 2000
<br>[[File:CoherentEEGAmbiguousFigureBinding.pdf]]
<br>[[File:CoherentEEGAmbiguousFigureBinding.pdf]]
<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>"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."
Line 17: Line 844:
[http://www.believermag.com/issues/200710/?read=article_aviv mind v brain, hobson v solms]
[http://www.believermag.com/issues/200710/?read=article_aviv mind v brain, hobson v solms]
<br>http://en.wikipedia.org/wiki/Activation-synthesis_hypothesis
<br>http://en.wikipedia.org/wiki/Activation-synthesis_hypothesis
<br>[[File:HobsomREMDreamProtoconsciousness.pdf|Hobson09ProtosconsciousnessREMDream]]


"Hobson and McCarley originally proposed in the 1970s that the differences in the waking-NREM-REM sleep cycle was the result of interactions between aminergic REM-off cells and cholinergic REM-on cells.[4] This was perceived as the activation-synthesis model, stating that brain activation during REM sleep results in synthesis of dream creation.[1][1] Hobson's five cardinal characteristics include: intense emotions, illogical content, apparent sensory impressions, uncritical acceptance of dream events, and difficulty in being remembered."
"Hobson and McCarley originally proposed in the 1970s that the differences in the waking-NREM-REM sleep cycle was the result of interactions between aminergic REM-off cells and cholinergic REM-on cells.[4] This was perceived as the activation-synthesis model, stating that brain activation during REM sleep results in synthesis of dream creation.[1][1] Hobson's five cardinal characteristics include: intense emotions, illogical content, apparent sensory impressions, uncritical acceptance of dream events, and difficulty in being remembered."
----
Berkeley Labs
[http://gallantlab.org/index.html Gallant Group]
<br>[http://walkerlab.berkeley.edu/ Walker Group]
<br>[http://socrates.berkeley.edu/~plab/ Palmer Group]
==Sleep Research==
[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2335403/ Comment on the AASM Manual for the Scoring of Sleep and Associated Events]


==random tangents==
==random tangents==
(following previous discussion) - we might select a few to study in more depth
(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.
(... or not!  Plenty more to explore - suggestions (random or otherwise) are welcome.
http://www.meltingasphalt.com/neurons-gone-wild/ --
Neurons Gone Wild - Levels of agency in the brain.


'''stereoscopic perception:'''
'''stereoscopic perception:'''
Line 44: Line 887:
==Previously==
==Previously==


[http://www.psychiclab.net/ Masahero's EEG Device/IBVA Software]
[http://www.psychiclab.net/ Masahiro's EEG Device/IBVA Software]
 
[http://www.instructables.com/id/open-brain-wave-interface-hardware-1/ and ... open source hardware design and kits on instructables.com]


[http://brainstorms.puzzlebox.info/ Puzzlebox - Opensource BCI Developers]
[http://brainstorms.puzzlebox.info/ Puzzlebox - Opensource BCI Developers]
Line 87: Line 932:


[[File:NeuroSkyCommunicationsProtocol.pdf‎]]
[[File:NeuroSkyCommunicationsProtocol.pdf‎]]
==Android Neutral Network Fuzzy Learning app==
[https://play.google.com/store/apps/details?id=com.faadooengineers.free_neuralnetworkandfuzzysystems Android Neutral Network Fuzzy Learning app in Play Store]
==Learning about Neural Networks==
* What type of network? [http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma RMB (Restricted Boltzmann Machine) vs Autoencoder/MLP vs CNN (Convolutional Neural Networks)]
* Andrej Karpathy's [http://cs.stanford.edu/people/karpathy/convnetjs/ Convolutional Neural Network coded in JavaScript (ConvNetJS)]
* Andrej Karpathy's [http://karpathy.github.io/2015/10/25/selfie/ What a Deep Neural Network thinks about your #selfie  (background on Convolutional Neural Networks for image recognition and classification)]
* [https://blog.webkid.io/neural-networks-in-javascript/ Neural Networks in JavaScript w/MNIST]
* [http://www.antoniodeluca.info/blog/10-08-2016/neural-networks-in-javascript.html Another NN in JS]
* [http://caza.la/synaptic/ The Synaptic "architecture-free" neural network library in JS]

Latest revision as of 00:02, 9 January 2020

This is essentially the groups meeting notes – a trail of bread crumbs of topics of conversation and projects entertained by the group

note: Learn more about previous neuro research at Noisebridge on the wiki... For example, the Analog_EEG_Amp page describes some project ideas and work done by others here in 2012

Websites and events that have piqued our interest[edit]

http://cs375.stanford.edu/ -- Dan Yamins Large-Scale Neural Network Models for Neuroscience CS375

https://faculty.washington.edu/chudler/facts.html -- brain facts

http://onlinehub.stanford.edu/cs224 -- Natural Language Processing with Deep Learning

http://neurable.com/

https://noisebridge.net/wiki/NBDSM -- noiseBridge Deepnet and Statistical Mechanics -- first meetup @ noisebridge 7/6/17 at 7PM

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

https://machinelearningguide.libsyn.com/rss -- machine learning guide podcast

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

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

http://alexandre.barachant.org/papers/

http://ncs.ethz.ch/publications -- neuromorphic cognitive systems

https://github.com/crillab/gophersat/blob/master/examples/sat-for-noobs.md -- SAT solvers

https://media.ccc.de/v/34c3-8948-low_cost_non-invasive_biomedical_imaging -- Open EIT 34c3 talk https://github.com/OpenEIT

http://acrovirt.org/ -- sensors

http://www.neuroeducate.com/ -- citizen neuroscience

https://www.youtube.com/watch?v=9mZuyUzyN4Q -- "Categories for the Working Hacker"

http://radicalsciencenews.org/599-2/ -- "Deep Learning Fuels Nvidia’s Self-Driving Car Technology"

https://arxiv.org/abs/1803.03635 -- "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" Jonathan Frankle, Michael Carbin

Global Workspace Theory[edit]

http://bernardbaars.pbworks.com/f/BaarsJCS1997.pdf -- "IN THE THEATRE OF CONSCIOUSNESS"


Symbolic Mathematics[edit]

https://arxiv.org/pdf/1912.01412.pdf -- "Deep Learning for Symbolic Mathematics"

https://www.scottaaronson.com/busybeaver.pdf -- "A Relatively Small Turing Machine Whose Behavior Is Independent of Set Theory"

https://www.scottaaronson.com/blog/?p=2725 -- "The 8000th Busy Beaver number eludes ZF set theory: new paper by Adam Yedidia and me"

Vision[edit]

https://webvision.med.utah.edu/

Language Models[edit]

https://blog.scaleway.com/2019/building-a-machine-reading-comprehension-system-using-the-latest-advances-in-deep-learning-for-nlp/ -- "Natural Language Processing: the age of Transformers"

https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf -- "Language Models are Unsupervised Multitask Learners"

https://arxiv.org/pdf/1706.03762 -- "Attention Is All You Need"

https://arxiv.org/pdf/1705.03122.pdf -- "Convolutional Sequence to Sequence Learning"

https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf -- "Sequence to Sequence Learning with Neural Networks"

https://arxiv.org/pdf/1508.07909.pdf -- "Neural Machine Translation of Rare Words with Subword Units"

https://github.com/rsennrich/subword-nmt

https://github.com/rowanz/grover - grover GPU/TPU based GPT-2 transformer implementation

https://arxiv.org/pdf/1905.12616.pdf -- "Defending Against Neural Fake News"

https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html - blog post on attention

https://github.com/lilianweng/transformer-tensorflow - sample implementation of "Attention Is All You Need"

https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py - "official" implementation of "Attention Is All You Need"

https://jalammar.github.io/illustrated-gpt2/ - The Illustrated GPT-2 (Visualizing Transformer Language Models)

Bioengineering[edit]

https://nips.cc/Conferences/2018/Schedule?showEvent=12487 -- "What Bodies Think About: Bioelectric Computation Outside the Nervous System, Primitive Cognition, and Synthetic Morphology"

Data Visualization[edit]

https://www.csc2.ncsu.edu/faculty/healey/download/tvcg.12b.pdf -- "Interest Driven Navigation in Visualization"

Fractal Dementia[edit]

https://pdfs.semanticscholar.org/0018/7c742e60d35d5034a63251e31e1b8d96c70b.pdf -- "Comparison of Fractal Dimension Algorithms for the Computation of Eeg Biomarkers for Dementia"

Brain Activity Dynamics[edit]

https://arxiv.org/pdf/1802.02523.pdf -- "Plasma Brain Dynamics (PBD): a Mechanism for EEG Waves Under Human Consciousness"

https://arxiv.org/pdf/1206.1108.pdf -- "Thermodynamic Model of Criticality in the Cortex Based On EEG/ECOG Data"

https://www.bm-science.com/images/bms/publ/art63.pdf -- "Topographic Mapping of Rapid Transitions in EEG Multiple Frequencies"

Silent Speech[edit]

https://dam-prod.media.mit.edu/x/2018/03/23/p43-kapur_BRjFwE6.pdf -- "AlterEgo: A Personalized Wearable Silent Speech Interface"

Image Reconstruction[edit]

https://www.biorxiv.org/content/biorxiv/early/2017/12/28/240317.full.pdf -- "Deep image reconstruction from human brain activity"

EEG Electrodes[edit]

https://sites.google.com/site/biofeedbackpages/velcro-sensors -- Saline electrodes

https://www.commsp.ee.ic.ac.uk/~mandic/Ear_EEG_IEEE_Pulse_2012.pdf -- "The In-the-Ear Recording Concept"

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8357918 -- "Dry-Contact Electrode Ear-EEG"

Generative Adversarial Networks (GAN)[edit]

https://arxiv.org/pdf/1710.08864 -- "One pixel attack for fooling deep neural networks"

Kolmolgorov Complexity[edit]

ftp://ftp.idsia.ch/pub/juergen/loconet.pdf -- "Discovering Neural Nets with Low Kolmolgorov Complexity and High Generalization Capability"

https://papers.nips.cc/paper/394-chaitin-kolmogorov-complexity-and-generalization-in-neural-networks.pdf -- "Chaitin-Kolmogorov Complexity and Generalization in Neural Networks"

OpenCV[edit]

http://arnab.org/blog/so-i-suck-24-automating-card-games-using-opencv-and-python

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.185.585&rep=rep1&type=pdf -- "Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm"

Category Theory[edit]

https://arxiv.org/pdf/1711.10455 -- "Backprop as Functor: A compositional perspective on supervised learning"

http://math.ucr.edu/home/baez/rosetta.pdf -- "Physics, Topology, Logic and Computation: A Rosetta Stone"

https://www.youtube.com/watch?v=BF6kHD1DAeU -- "Category theory foundations 1.0 — Steve Awodey"

Proof Searcher[edit]

https://arxiv.org/pdf/cs/0207097 -- "Optimal Ordered Problem Solver"

http://people.idsia.ch/~juergen/ultimatecognition.pdf -- "Ultimate Cognition a la Gödel"

http://people.idsia.ch/~juergen/selfreflection.pdf -- "Towards an Actual Gödel Machine Implementation"

Capsule Models[edit]

https://arxiv.org/pdf/1710.09829.pdf -- "Dynamic Routing Between Capsules"

https://openreview.net/pdf?id=HJWLfGWRb -- "Matrix Capsules with EM Routing"

Multivariate Coherence Training[edit]

https://www.youtube.com/watch?v=qGYjvLki0WY

Infrared Neuroimaging[edit]

http://www.ecse.rpi.edu/~yazici/bio_book.pdf -- "Near Infrared Imaging and Spectroscopy for Brain Activity Monitoring"

http://fangyenlab.seas.upenn.edu/pubs/isr.pdf -- "Intrinsic optical signals in neural tissues: measurements, mechanisms, and applications"

Geometry[edit]

http://arxiv.org/abs/1710.10784 -- "How deep learning works --The geometry of deep learning"

Affective Computing[edit]

http://affect.media.mit.edu/pdfs/05.ahn-picard-acii.pdf -- "Affective-Cognitive Learning and Decision Making: A Motivational Reward Framework For Affective Agents"

Explainability[edit]

http://arxiv.org/abs/1708.01785 -- "Interpreting CNN knowledge via an Explanatory Graph"

NLP[edit]

https://arxiv.org/pdf/1605.06640 -- "Programming with a Differentiable Forth Interpreter"

https://pdfs.semanticscholar.org/f683/dbe8a22d633ad3a2cff379b055b26684a838.pdf -- "Solving General Arithmetic Word Problems"

https://arxiv.org/pdf/1611.04558.pdf -- "Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation"

http://emnlp2014.org/papers/pdf/EMNLP2014162.pdf -- "GloVe: Global Vectors for Word Representation"

RNNs[edit]

https://arxiv.org/pdf/1611.01576.pdf -- "Quasi Recurrent Neural Networks"

Hyper-parameter Optimization[edit]

https://arxiv.org/abs/1603.06560 -- "Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization"

Transfer Learning[edit]

http://arxiv.org/abs/1710.10776v1 -- "Transfer Learning to Learn with Multitask Neural Model Search"

Reinforcement Learning[edit]

http://www2.hawaii.edu/~sstill/StillPrecup2011.pdf -- "An information-theoretic approach to curiosity-driven reinforcement learning"

https://arxiv.org/abs/1605.06676 -- "Learning to Communicate with Deep Multi-Agent Reinforcement Learning"

Learning to Learn[edit]

https://arxiv.org/pdf/1703.01041.pdf -- "Large-Scale Evolution of Image Classifiers"

https://arxiv.org/pdf/1611.01578 -- "Neural Architecture Search with Reinforcement Learning"

The Utility of "Noise" in ML[edit]

https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf -- "Dropout: A Simple Way to Prevent Neural Networks from Overfitting"

http://yann.lecun.com/exdb/publis/pdf/lecun-90b.pdf -- "Optimal Brain Damage"

https://arxiv.org/pdf/1502.01852.pdf -- "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"

One-shot learning[edit]

https://arxiv.org/abs/1605%2E06065 -- "One-shot Learning with Memory-Augmented Neural Networks"

Program Synthesis[edit]

https://pdfs.semanticscholar.org/0163/35ce7e0a073623e1deac7138b28913dbf594.pdf -- "Human-level concept learning through probabilistic program induction"

https://arxiv.org/pdf/1511.06279.pdf -- "Neural Programmer: Inducing Latent Programs with Gradient Descent"

https://arxiv.org/abs/1608.04428 -- "TerpreT: A Probabilistic Programming Language for Program Induction" Gaunt et al 2016

Machine Learning Interaction[edit]

https://teachablemachine.withgoogle.com/#

Game Theory[edit]

https://arxiv.org/abs/1707.01068v1 - Maintaining cooperation in complex social dilemmas using deep reinforcement learning

Questions of Physics and Free Will[edit]

http://www.scottaaronson.com/papers/giqtm3.pdf - The Ghost in the Quantum Turing Machine

CNN[edit]

https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/ - "A Beginner's Guide To Understanding Convolutional Neural Networks"

https://adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/ - "A Beginner's Guide To Understanding Convolutional Neural Networks Part 2"

http://scs.ryerson.ca/~aharley/vis/harley_vis_isvc15.pdf -- "An Interactive Node-Link Visualization of Convolutional Neural Networks"

http://white.stanford.edu/teach/index.php/An_Introduction_to_Convolutional_Neural_Networks

http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf -- "Learning to Generate Chairs With Convolutional Neural Networks"

http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf -- "What's Wrong With Deep Learning?"

Mind-Body Relations[edit]

http://www.pnas.org/content/111/20/7379.full.pdf -- "Voluntary activation of the sympathetic nervous system and attenuation of the innate immune response in humans"

Math[edit]

https://arxiv.org/pdf/1311.1090.pdf -- "Polyhedrons and Perceptrons Are Functionally Equivalent"

Example code and training data using polyhedrons developed by author of above paper: https://www.noisebridge.net/wiki/DreamTeam#Code

Bayesian Inference[edit]

https://noisebridge.net/images/e/ef/Perception_is_in_the_Details12.pdf -- "Perception is in the Details: A Predictive Coding Account of the Psychedelic Phenomenon"

http://rsif.royalsocietypublishing.org/content/10/86/20130475 -- "Life as we know it"

http://jmlr.csail.mit.edu/proceedings/papers/v31/wang13b.pdf -- "Collapsed Variational Bayesian Inference for Hidden Markov Models"

http://www.datalab.uci.edu/papers/nips06_cvb.pdf -- "A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation"

http://www.cnbc.cmu.edu/~tai/papers/lee_mumford_josa.pdf -- "Hierarchical Bayesian inference in the visual cortex"

https://www.researchgate.net/profile/Til_Bergmann/publication/262423308_Temporal_coding_organized_by_coupled_alpha_and_gamma_oscillations_prioritize_visual_processing/links/0deec537d1bfda474c000000/Temporal-coding-organized-by-coupled-alpha-and-gamma-oscillations-prioritize-visual-processing.pdf -- "Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing"

http://www.cell.com/neuron/pdf/S0896-6273(15)00823-5.pdf -- "Rhythms for Cognition: Communication through Coherence"

http://www.biorxiv.org/content/biorxiv/early/2014/05/06/004804.full.pdf -- "Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels"

Speech Recognition[edit]

https://arxiv.org/pdf/1612.00694v1 -- "ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA"

Sound Classification[edit]

https://arxiv.org/pdf/1608.04363v2 -- "Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification"

https://arxiv.org/pdf/1605.09507 "Deep convolutional neural networks for predominant instrument recognition in polyphonic music"

Hardware Implementations - FPGA, GPU, etc[edit]

https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf -- "Artificial neural networks in hardware: A survey of two decades of progress"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.133.9185&rep=rep1&type=pdf "A Self-Repairing Multiplexer-Based FPGA Inspired by Biological Processes"

http://www.genetic-programming.com/jkpdf/fpga1998.pdf -- "Evolving Computer Programs using Rapidly Reconfigurable Field-Programmable Gate Arrays and Genetic Programming"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.2588&rep=rep1&type=pdf -- "Flexible Implementation of Genetic Algorithms on FPGAs"

http://www.users.muohio.edu/jamiespa/html_papers/gem_10.pdf -- "Revisiting Genetic Algorithms for the FPGA Placement Problem"

https://arxiv.org/pdf/1609.09296v1 -- "Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs"

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.7533&rep=rep1&type=pdf -- "FPGA-TARGETED NEURAL ARCHITECTURE FOR EMBEDDED ALERTNESS DETECTION"

https://arxiv.org/pdf/1611.02450v1 -- "PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks"

https://arxiv.org/pdf/1605.06402v1 -- "Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks"

https://homes.cs.washington.edu/~luisceze/publications/snnap-hpca-2015.pdf -- "SNNAP: Approximate Computing on Programmable SoCs via Neural Acceleration"

https://arxiv.org/pdf/1701.00485v2 -- "Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices"

VLSI[edit]

http://ncs.ethz.ch/pubs/pdf/Indiveri_etal06.pdf -- "A VLSI array of low-power spiking neurons and bistable synapses with spike–timing dependent plasticity"

Pruning[edit]

http://papers.nips.cc/paper/5784-learning-both-weights-and-connections-for-efficient-neural-network.pdf -- "Learning both Weights and Connections for Efficient Neural Networks"

https://arxiv.org/pdf/1701.04465 -- "The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning"

https://arxiv.org/pdf/1512.08571 -- "Structured Pruning of Deep Convolutional Neural Networks"

https://arxiv.org/pdf/1611.01427 -- "Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks"

Efficient Neural Networks via Compression, Quantization, Model Reduction, etc[edit]

https://arxiv.org/pdf/1504.04788 -- "Compressing Neural Networks with the Hashing Trick"

https://arxiv.org/pdf/1509.08745 -- "Compression of Deep Neural Networks on the Fly"

https://arxiv.org/pdf/1502.03436 -- "An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections"

https://arxiv.org/pdf/1510.00149 -- "Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding"

https://arxiv.org/pdf/1612.00891 -- "Parameter Compression of Recurrent Neural Networks and Degradation of Short-term Memory"

https://arxiv.org/pdf/1609.07061 -- "Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations"

https://arxiv.org/pdf/1607.05418 -- "Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off"

https://arxiv.org/pdf/1602.08194 -- "Scalable and Sustainable Deep Learning via Randomized Hashing"

https://arxiv.org/pdf/1508.05463 -- "StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity"

https://arxiv.org/pdf/1412.7024 -- "Training Deep Neural Networks with Low Precision Multiplications"

https://arxiv.org/pdf/1612.03940 -- "Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks"

https://arxiv.org/pdf/1609.00222 -- "Ternary Neural Networks for Resource-Efficient AI Applications"

Neural Network Hyperparameter Optimization[edit]

https://arxiv.org/pdf/1601.00917 -- "DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks"

Neural Network based EEG Analysis[edit]

end http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"

http://inter-eng.upm.ro/2012/files/proceedings/papers/paper72.pdf -- "Neural Network Parallelization on FPGA Platform for EEG Signal Classification"

Seizure Detection[edit]

also see https://noisebridge.net/wiki/Kaggle for a (September 2016) current project!

and https://github.com/kevinjos/kaggle-aes-seizure-prediction (some earlier exploration, November 2014)

(broken link, sorry) http://www.sersc.org/journals/ijsip/vol7_no5/26.pdf -- "A Neural Network Model for Predicting Epileptic Seizures based on Fourier-Bessel Functions"

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230664/pdf/nihms-34528.pdf -- "A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models"

(another broken link) http://cs.uni-muenster.de/Professoren/Lippe/diplomarbeiten/html/eisenbach/Untersuchte%20Artikel/PPHD+00.pdf -- "Recurrent neural network based preenddiction of epileptic seizures in intra- and extracranial EEG"

Visible Light Sensor Network[edit]

http://infoteh.etf.unssa.rs.ba/zbornik/2016/radovi/KST-1/KST-1-15.pdf -- "Analysis of Visible Light Communication System for Implementation in Sensor Networks"

Neurophysiology[edit]

http://www.buzsakilab.com/content/PDFs/BuzsakiKoch2012.pdf -- "The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes"

Signal Processing[edit]

http://www.ti.com/lit/an/slyt438/slyt438.pdf -- "How delta-sigma ADCs work, Part 2"

http://provideyourown.com/2011/analogwrite-convert-pwm-to-voltage/ -- "Arduino’s AnalogWrite – Converting PWM to a Voltage"

http://sim.okawa-denshi.jp/en/PWMtool.php -- "RC Low-pass Filter Design for PWM (Transient Analysis Calculator)"

Hyperdimensional Computing[edit]

http://redwood.berkeley.edu/pkanerva/papers/kanerva09-hyperdimensional.pdf -- "Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors"

http://arxiv.org/pdf/1602.03032.pdf -- "Associative Long Short-Term Memory"

Bird Flocks and Maximum Entropy[edit]

http://arxiv.org/pdf/1107.0604v1 -- "Statistical Mechanics and Flocks of Birds"

https://arxiv.org/pdf/1311.2319.pdf -- "Statistical Mechanics of Surjective Cellular Automata"

https://pdfs.semanticscholar.org/fa84/5d15a54e99519d83a3ae1510200dc2eca471.pdf -- "Inhomogeneous Cellular Automata and Statistical Mechanics"

http://arxiv.org/pdf/1307.5563v1 -- "Social interactions dominate speed control in driving natural flocks toward criticality"

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/MIT6_050JS08_penfield.pdf -- "Information and Entropy (Course Notes)"

Whale Songs[edit]

https://arxiv.org/pdf/1307.0589.pdf -- "The Orchive : Data mining a massive bioacoustic archive"

https://www.researchgate.net/profile/Herbert_Roitblat/publication/13429327_The_neural_network_classification_of_false_killer_whale_%28Pseudorca_crassidens%29_vocalizations/links/540d2ff60cf2df04e75478cd.pdf -- "The neural network classification of false killer whale (Pseudorca crassidens) vocalizations"

http://users.iit.demokritos.gr/~paliourg/papers/PhD.pdf -- "REFINEMENT OF TEMPORAL CONSTRAINTS IN AN EVENT RECOGNITION SYSTEM USING SMALL DATASETS"

https://www.nersc.no/sites/www.nersc.no/files/master_thesis_sebastian_menze.pdf -- "Estimating fin whale distribution from ambient noise spectra using Bayesian inversion"

http://sis.univ-tln.fr/~glotin/IJCNN2015_IHMMbioac_BartChamGlot.pdf -- "Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis"

https://www.inf.ed.ac.uk/publications/thesis/online/IM030057.pdf -- "Hidden Markov Model Clustering of Acoustic Data"

http://danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/ -- Using deep learning to listen for whales

Computational Cognitive Neuroscience[edit]

http://www.pnas.org/content/110/41/16390.full -- "Indirection and symbol-like processing in the prefrontal cortex and basal ganglia"

http://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/docs/jaf.pdf -- "Connectionism and Cognitive Architecture: A Critical Analysis"

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2728678/pdf/nihms131814.pdf -- Neves et al 2008 "Cell Shape and Negative Links in Regulatory Motifs Together Control Spatial Information Flow in Signaling Networks"

http://psych.colorado.edu/~oreilly/papers/AisaMingusOReilly08.pdf -- "The Emergent Neural Modeling System"

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

Text Generation[edit]

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

Games[edit]

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"

https://www.youtube.com/watch?v=k2rgzZ2WXKo -- "Best Practices for Procedural Narrative Generation" Chris Martens

Large Scale Brain Simulation[edit]

http://www.nowere.net/b/arch/96550/src/1378907656268.pdf -- "A world survey of artificial brain projects, Part I: Large-scale brain simulations"

Music[edit]

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

Code[edit]

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

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

https://github.com/Micah1/neurotech (brainduino code)

Hidden Markov Models[edit]

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[edit]

http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf -- "Long Short-Term Memory"

ftp://ftp.idsia.ch/pub/juergen/2002_icannMusic.pdf -- "Learning The Long-Term Structure of the Blues"

http://www.overcomplete.net/papers/nn2012.pdf -- "A generalized LSTM-like training algorithm for second-order recurrent neural networks"

http://www.iro.umontreal.ca/~eckdoug/papers/2003_nn.pdf -- "Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets"

http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html -- "Long Short-Term Memory dramatically improves Google Voice etc"

https://arxiv.org/pdf/1511.05552v4.pdf -- "Recurrent Neural Networks Hardware Implementation on FPGA"

http://vast.cs.ucla.edu/sites/default/files/publications/ASP-DAC2017-1352-11.pdf -- "FPGA-based Accelerator for Long Short-Term Memory Recurrent Neural Networks"

Question Answering[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf -- "Neural Networks - A Systematic Introduction"

http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf (works through simple example)

http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/

http://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf

also see http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf (entire book "Neural Networks - a Systemic Introduction" by Raul Rojas)

http://work.caltech.edu/lectures.html Hoeffding's inequality, VC Dimension and Back Propagation ANN

http://www.cs.stir.ac.uk/research/publications/techreps/pdf/TR148.pdf ("Learning XOR: exploring the space of a classic problem")

http://www.sysc.pdx.edu/classes/Werbos-Backpropagation%20through%20time.pdf -- "Backpropagation Through Time: What it Does and How to Do It"

Computer Vision[edit]

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)[edit]

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[edit]

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

Spiking Neural Networks[edit]

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

http://apt.cs.manchester.ac.uk/ftp/pub/apt/papers/JavN_ICS09.pdf -- "Understanding the Interconnection Network of SpiNNaker"

Hierarchical Temporal Memory[edit]

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[edit]

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"

Mixture of Experts[edit]

http://www.cs.toronto.edu/~fritz/absps/jjnh91.pdf -- "Adaptive Mixtures of Local Experts"

Hopfield nets and RBMs[edit]

http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf -- Yoshua Bengio, 2009, Learning Deep Architectures for AI

http://deeplearning.cs.cmu.edu/ -- Syllabus for cs course on deep learning, possible source of literature for the library

https://class.coursera.org/neuralnets-2012-001/lecture -- Lecture 11 - 15 to aid in understanding of Hinton 2006 paper above

http://www.pnas.org/content/79/8/2554.full.pdf -- "Neural networks and physical systems with emergent collective computational abilities" (Hopfield 1982)

http://page.mi.fu-berlin.de/rojas/neural/chapter/K13.pdf -- "The Hopfield Model" (Rojas 1996)

http://www.seas.upenn.edu/~wulsin/docs/wulsin2010.pdf -- "Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets"

http://www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/cameraready_xwjiabibe.pdf -- "A Novel Semi-supervised Deep Learning Framework for Affective State Recognition on EEG Signals"

http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf -- "A Practical Guide to Training Restricted Boltzmann Machines"

http://arxiv.org/pdf/1503.07793v2.pdf -- "Gibbs Sampling with Low-Power Spiking Digital Neurons"

http://arxiv.org/pdf/1311.0190v1 -- "On the typical properties of inverse problems in statistical mechanics" Iacopo Mastromatteo 2013

http://jmlr.org/proceedings/papers/v5/salakhutdinov09a/salakhutdinov09a.pdf -- "Deep Boltzmann Machines" Salakhutdinov & Hinton 2009

http://www.cs.toronto.edu/~hinton/absps/tr00-004.pdf -- "Training Products of Experts by Minimizing Contrastive Divergence"

http://www.eecg.toronto.edu/~pc/research/publications/ly.fpga2009.submitted.pdf -- "A High-Performance FPGA Architecture for Restricted Boltzmann Machines" Ly & Chow 2009

https://pdfs.semanticscholar.org/85fa/f7c3c05388e2bcd097a416606bdd88fc0c7c.pdf -- "A MULTI-FPGA ARCHITECTURE FOR STOCHASTIC RESTRICTED BOLTZMANN MACHINES" Ly & Chow 2009

Variational Renormalization[edit]

https://arxiv.org/pdf/1410.3831 -- "An exact mapping between the Variational Renormalization Group and Deep Learning"

Neuromorphic Stuff[edit]

https://arxiv.org/pdf/1508.01008.pdf -- "INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks" Chung, Shin & Kang 2015

Markov Chain Monte Carlo[edit]

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[edit]

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[edit]

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[edit]

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

Sleep Research[edit]

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

random tangents[edit]

(following previous discussion) - we might select a few to study in more depth (... or not! Plenty more to explore - suggestions (random or otherwise) are welcome. http://www.meltingasphalt.com/neurons-gone-wild/ -- Neurons Gone Wild - Levels of agency in the brain.


stereoscopic perception:


some (maybe) interesting background on Information Theory (cool title...)

Claude Shannon: "Communication in the Presence of Noise"
File:Shannon noise.pdf
"We will call a system that transmits without errors at the rate C an ideal system.
 Such a system cannot be achieved with any finite encoding process
 but can be approximated as closely as desired."

wikipedia etc quick reads:

https://en.wikipedia.org/wiki/Eeg
https://en.wikipedia.org/wiki/Neural_synchronization
https://en.wikipedia.org/wiki/Event-related_potentials
http://www.scholarpedia.org/article/Spike-and-wave_oscillations
http://www.scholarpedia.org/article/Thalamocortical_oscillations

Previously[edit]

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.

Background Reading[edit]

http://nanosouffle.net/ (view into Arxiv.org)

Name: Hunting for Meaning after Midnight, Miller 2007

URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0002.pdf>

Name: Broken mirrors, Ram, VS, & Oberman, LM, 2006, Nov

URL: <http://www.noisebridge.net/pipermail/neuro/attachments/20130501/4b992eb6/attachment-0003.pdf>

Ramachandran Critique

http://blogs.scientificamerican.com/guest-blog/2012/11/06/whats-so-special-about-mirror-neurons/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2773693/

Sleep/Dream Studies

http://www.cns.atr.jp/dni/en/publications/

NeuroSky Docs[edit]

File:NeuroSkyDongleProtocol.pdf

File:NeuroSkyCommunicationsProtocol.pdf

Android Neutral Network Fuzzy Learning app[edit]

Android Neutral Network Fuzzy Learning app in Play Store

Learning about Neural Networks[edit]