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An awesome list of computational neuroscience and computational cognitive science.
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An awesome list of computational neuroscience and computational cognitive science.

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# Awesome Computational Neuroscience

An awesome list of computational neuroscience and computational cognitive science. I build this list only for my own use. I used to use bookmarks. But it is a bad way to organize things, I can hardly find what I want. 



Other useful [Computational neuroscience resources](http://neural-reckoning.org/comp-neuro-resources.html).



"DISCLAIMER: The contents of this list reflect my own personal interests and should not be taken as a recommendation or endorsement of any kind. Use of any information or resources provided in this list is at your own risk."



- [Course](#course)

  * [Computational Neuroscience](#computational-neuroscience)

  * [Computational Cognitive Science](#computational-cognitive-science)

  * [Neuroscience](#neuroscience)

  * [Psychology](#psychology)

- [Package](#package)

  * [Computational Neuroscience](#computational-neuroscience-1)

  * [Computational Cognitive Science](#computational-cognitive-science-1)

  * [Machine Learning](#machine-learning)

- [Talk](#talk)

- [Summer School](#summer-school)

- [Tutorial](#tutorial)

- [Books](#books)

- [Open Data](#open-data)

- [Podcast](#podcast)



Table of contents generated with markdown-toc



## Course



### Computational Neuroscience



- [Computational Neuroscience](https://www.coursera.org/learn/computational-neuroscience) - Coursera

- [Computational Neuroscience: the basics](https://training.incf.org/course/computational-neuroscience-basics) - INCF

- [Cajal Course Computational Neuroscience](https://training.incf.org/course/cajal-course-computational-neuroscience) - INCF

- [Neuronal Dynamics](https://neuronaldynamics.epfl.ch/) - EPFL, Wulfram Gerstner

- [Introduction to Neural Computation](https://www.youtube.com/playlist?list=PLUl4u3cNGP61I4aI5T6OaFfRK2gihjiMm) - MIT 9.40, Michale Fee, Spring 2018

- [Neuromatch Academy](https://compneuro.neuromatch.io/tutorials/intro.html) - Computational Neuroscience

- [Understanding Vision: theory, models, and data](https://www.youtube.com/playlist?list=PLbG9iu2mq65-Vmo9VRtkh9AXJ2Ekfrqtk) - Li Zhaoping

- [Computational Neuroscience](https://www.youtube.com/playlist?list=PLX-XEf1yTMrkcpni8RJMnqGBlA9uEHlaE) - SYDE 552, Terry Stewart, Winter 2021

- [Dynamical Systems in Neuroscience](https://www.youtube.com/playlist?list=PLTEtXsHFKZTvNIoeXHF5JSVc21LrM-j2f) - NeuroLogos

- [The biophysical basis of neurons and networks](https://neurophysics.ucsd.edu/physics_178_278.php) - UCSD Physics 178/278, David Kleinfeld

- [Neural Computation](https://redwood.berkeley.edu/courses/vs265/) - UCB VS265, Bruno Olshausen

- [Gatsby Unit Course Materials](http://www.gatsby.ucl.ac.uk/teaching/courses/)



### Computational Cognitive Science



- [Modeling the Mind](http://u.arizona.edu/~bob/web_NSCS344/), UA NSCS 344, Robert Wilson, 2020

- [How to build a brain from scratch](https://humaninformationprocessing.com/teaching/) - UCL, Chris Summerfield

- [Beginners guide to doing experimental cognitive science research](https://vimeo.com/showcase/howtocogsci) - Todd Gureckis

- [Computational Psychiatry](https://video.ethz.ch/lectures/d-itet/2021/autumn/227-0971-00L/8007a432-d2bc-4836-8371-8d11048b537d.html) - ETH Zurich, Autumn 2021

- [Computational Cognitive Neuroscience](https://www.youtube.com/playlist?list=PLu02O8xRZn7xtNx03Rlq6xMRdYcQgEpar) - UC Davis, Randall O'Reilly, Spring 2020

- [Bayesian Statistics and Hierarchical Bayesian Modeling](https://www.youtube.com/playlist?list=PLfRTb2z8k2x8ZCqDJ0WEFNs2ymXQCliLa) - Lei Zhang

- [Computational cognitive modeling]() - NYU PSYCH-GA 3405.004 / DS-GA 1016.003, Brenden Lake, Spring 2023

- [Advancing AI through cognitive science](https://brendenlake.github.io/AAI-site/) - NYU PSYCH-GA 3405.001 / DS-GA 3001.014, Brenden Lake, Spring 2019



### Neuroscience



- [The Human Brain](https://www.youtube.com/playlist?list=PLyGKBDfnk-iAQx4Kw9JeVqspbg77sfAK0) - MIT 9.11, Nancy Kanwisher, Spring 2018 

- [Neuroscience and Neuroimaging](https://www.coursera.org/specializations/computational-neuroscience) - Coursera

- [Intro to fMRI class](https://www.youtube.com/playlist?list=PLDcxb_BvQtAoA2ZPmvL176hJmNf8c5ABB) - Rajeev Raizada

- [fMRI Bootcamp](https://cbmm.mit.edu/fmri-bootcamp)

- [Human Behavioral Biology](https://www.youtube.com/playlist?list=PL848F2368C90DDC3D) - Stanford, Robert Sapolsky

- [Neural Data Science](https://www.youtube.com/playlist?list=PL05umP7R6ij3SxudmSWFL_zGh0BMrRdrx) - Tübingen, Philipp Berens, 2021

- [Introduction to Brain and Consciousness](https://www.youtube.com/playlist?list=PLw86EyOzTC2O-ANg5uj2yOAEqmN2kSm0d)

- [Grossbergian Neuroscience](https://www.youtube.com/playlist?list=PLTEtXsHFKZTsxmKyVn69ZmLBxghBH1tNR) - NeuroLogos

- [Dynamic Data Visualization Workshop](https://dynamicdataviz.github.io/) - An NIMH-hosted workshop on principles, tools, and approaches to constructing effective dynamic data visualizations

- [Introduction to ERPs](https://www.youtube.com/playlist?list=PLXKXgcv8muTKbyGsFVQSIeF5InZfrkt2M) - Steve Luck

- [Analyzing Neural Time Series Data](https://www.youtube.com/@mikexcohen1/playlists) - Mike X Cohen

- [NIH fMRI Courses](https://fmrif.nimh.nih.gov/public/)



### Psychology

- [Philosophical Psychology](https://meehl.umn.edu/video) - UMN, Paul Meehl, Winter 1989

- [Computational Reason](https://www.youtube.com/playlist?list=PLTEtXsHFKZTt_8pbhVMxQYGO1Vx-qPlWA) - NeuroLogos



## Package



### Computational Neuroscience



- [BrainPy](https://github.com/brainpy/BrainPy) - A flexible, efficient, and extensible framework for computational neuroscience and brain-inspired computation based on the JIT compilation.

- [Nengo](https://github.com/hmmlearn/hmmlearn) - The Nengo Brain Maker is a Python package for building, testing, and deploying neural networks.

- [NeuroGym](https://github.com/neurogym/neurogym) - NeuroGym is a curated collection of neuroscience tasks with a common interface. The goal is to facilitate training of neural network models on neuroscience tasks.



### Computational Cognitive Science



- [HDDM](https://hddm.readthedocs.io/en/latest/) - A python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC).

- [Computational and Behavioral Modeling](https://payampiray.github.io/cbm) - CBM provides tools for hierarchical Bayesian inference

- [rlssm](https://rlssm.readthedocs.io/en/latest/index.html) - A Python package for fitting reinforcement learning models, sequential sampling models, and combinations of the two, using Bayesian parameter estimation.

- [RL_DDM](https://github.com/sjgershm/RL_DDM) - Reinforcement learning + drift-diffusion model repository.

- [Bandits](https://github.com/bgalbraith/bandits) - Python library for Multi-Armed Bandits implements the following algorithms: Epsilon-Greedy, UCB1, Softmax, Thompson Sampling

- [NivTurk](https://nivlab.github.io/nivturk/) - Niv lab tools for securely serving and storing data from online computational psychiatry experiments.



### Machine Learning



- [Tianshou](https://tianshou.readthedocs.io/en/stable/index.html) - A reinforcement learning platform based on pure PyTorch.

- [Variational Bayesian Monte Carlo](https://github.com/acerbilab/vbmc) - VBMC is an approximate inference method designed to fit and evaluate computational models with a limited budget of potentially noisy likelihood evaluations.

- [BADS](https://github.com/acerbilab/bads) - BADS is a fast hybrid Bayesian optimization algorithm designed to solve difficult optimization problems, in particular related to fitting computational models



## Talk

- [MIT CBMM](https://www.youtube.com/@MITCBMM)

- [Cosyne](https://www.youtube.com/channel/UCzOTbZTHTubFNjANAR33AAg)

- [CCN](https://www.youtube.com/@cognitivecomputationalneur7223/playlists)

- [CCN 2017](https://www.youtube.com/@kendrickkay/videos)

- [Harvard Machine Learning Foundations Group](https://mltheory.org/)

- [Theoretical Neuroscience](https://www.youtube.com/@theoreticalneuroscience6062/videos)

- [CogSci](https://www.youtube.com/@cogsciinterdisciplinarystu2501/videos)

- [Simons Institute](https://www.youtube.com/@SimonsInstituteTOC/videos)

- [UCL NeuroAI](https://www.ucl.ac.uk/research/domains/neuroscience/ucl-neuroai)

- [RTG Computational Cognition](https://www.youtube.com/channel/UCSGkTC1wXU6h6jGOyviuKWA/videos)

- [MRC Cognition and Brain Sciences Unit](https://www.youtube.com/@MRCCBU/videos)

- [Spiking Neural networks as Universal Function Approximators](https://www.youtube.com/@neuralreckoning) - 

- [MBL Brains, Minds and Machines](https://mbl.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx)

- [NeurIPS 2022](https://slideslive.com/neurips-2022)

- [Meaning of Life Symposium](https://www.youtube.com/playlist?list=PLypiXJdtIca7i8IrNye4IenjnUCP9LF35)

- [Dynamic Field Theory](https://www.youtube.com/@dynamicfieldtheory7915/videos)

- [SFN Annual Meeting](https://neuronline.sfn.org/collection/sfn-annual-meeting-recordings)



## Summer School

- [Marine Biological Laboratory](https://www.mbl.edu/education/advanced-research-training-courses/course-offerings)

- [Cold Spring Harbor Asia](http://www.csh-asia.org/)

- [Graduate Workshop in Computational Social Science at Santa Fe Institute](https://www.santafe.edu/engage/learn/programs/graduate-workshop-computational-social-science)

- [NeuroHackademy](https://neurohackademy.org/)



## Tutorial



Other tutorials see [Online Resources for Systems and Computational Neuroscience](https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience/)



Intro:



- [MIT fMRI Bootcamp](https://www.youtube.com/playlist?list=PLyGKBDfnk-iDVpUGSR_GlDmQrZOS0Lk6k)

- [Python Data Science](https://jakevdp.github.io/PythonDataScienceHandbook/) - Jake VanderPlas

- [Reproducible Data Analysis in Jupyter](https://www.youtube.com/playlist?list=PLYCpMb24GpOC704uO9svUrihl-HY1tTJJ) - Jake Vanderplas

- [DartBrains](https://dartbrains.org/content/intro.html)

- [RBootcamp](https://r-bootcamp.netlify.app/)

- [Data Science](https://trenton3983.github.io/) - Trenton McKinney

- [Learning Machine](https://rentruewang.github.io/learning-machine/intro.html) - RenChu Wang

- [Models of Learning](http://www.hannekedenouden.ruhosting.nl/RLtutorial/Instructions.html) - Hanneke den Ouden

- [Bayesian Model](https://www.sumsar.net/blog/2015/11/a-bayesian-model-to-calculate-whether-my-wife-is-pregnant/) - Rasmus Bååth

- [PyTorch - Python Deep Learning](https://www.youtube.com/playlist?list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG) - deeplizard

- [Andy’s Brain Book](https://andysbrainbook.readthedocs.io/en/latest/index.html)

- [BrainIAK Tutorial](https://github.com/brainiak/brainiak-tutorials/wiki/Resources)

- [Python and Matlab programs for fMRI](https://www2.bcs.rochester.edu/sites/raizada/fmri-matlab.html)

- [RSA Workshop](https://colab.research.google.com/drive/1UEtFr-oJisRzl8BmzbNdMZZ7-Of0gLcH?usp=sharing#scrollTo=WCGDnFNYIDGT)

- [Quantitative Neuroscience](https://github.com/BlohmLab/NSCI801-QuantNeuro)

- [Python for the practicing neuroscientist](https://elifesciences.org/labs/f779833b/python-for-the-practicing-neuroscientist-an-online-educational-resource)

- [The Multi-Armed Bandit Problem and Its Solutions](https://lilianweng.github.io/posts/2018-01-23-multi-armed-bandit/)

- [AI Wiki](https://wiki.pathmind.com/)

- [Machine Learning Mastery](https://machinelearningmastery.com/)

- [Essential Math for Data Science](https://hadrienj.github.io/)

- [What does MEG measure?](https://www.nature.com/scitable/blog/brain-metrics/) :sob:

- [Learn Shell](https://www.learnshell.org/)

- [Interactive Vim tutorial](https://www.openvim.com/)

- [Advanced Cognitive Modeling Notes](https://fusaroli.github.io/AdvancedCognitiveModeling2023/)

- [Better Markov Chain](https://elevanth.org/blog/2017/11/28/build-a-better-markov-chain/)



Advance:



- [Probabilistic Models of Cognition](https://probmods.org/) - Noah Goodman & Joshua Tenenbaum

- [Recurrent neural networks for cognitive neuroscience](https://www.youtube.com/watch?v=k5bQnPtX3wY) - Guangyu Yang

- [Artificial neural networks for neuroscientists](https://github.com/gyyang/nn-brain) - Guangyu Yang

- [Recurrent Neural Network Tutorial](https://www.rajanlab.com/cosyne2021) - Kanaka Rajan

- [Spiking Neural Networks Tutorial](https://www.youtube.com/playlist?list=PL09WqqDbQWHGJd7Il3yVxiBts5nRSxvJ4) - Dan Goodman

- [Modeling reinforcement learning](https://speekenbrink-lab.github.io/blog/) - Maarten Speekenbrink

- [Introduction to Neural Network Models of Cognition](https://com-cog-book.github.io/com-cog-book/intro.html) - Pablo Caceres

- [Computational Models of Human Social Behavior and Neuroscience](https://shawnrhoads.github.io/gu-psyc-347/) - Shawn A Rhoads

- [Spinning Up in Deep RL](https://spinningup.openai.com/en/latest/) - OpenAI

- [Linear Algebra for Theoretical Neuroscience](http://www.columbia.edu/cu/neurotheory/Ken/math-notes/) - Ken Miller

- [Modeling in Neuroscience](http://compneurosci.com/wiki/index.php?title=CoSMo_2018) - Gunnar Blohm

- [Data Skills for Neuroscientists](https://neuronline.sfn.org/scientific-research/data-science-and-data-skills-for-neuroscientists) - SfN

- [Statistical tools for high-throughput data analysis](http://www.sthda.com/english/)

- [Computational and Inferential Thinking: The Foundations of Data Science](https://inferentialthinking.com/chapters/intro.html)

- [Kalman Filter Tutorial](https://www.kalmanfilter.net/default.aspx)

- [Bayesian Deep Learning and Probabilistic Model Construction](https://www.youtube.com/watch?v=E1qhGw8QxqY)

- [Deep Reinforcement Learning with Pytorch](https://github.com/sweetice/Deep-reinforcement-learning-with-pytorch)

- [Basic Examples for Reinforcement Learning](https://github.com/ucla-rlcourse/RLexample)

- [Statistical models for neural data](https://www.youtube.com/watch?v=NFeGW5ljUoI&list=PL9YzmV9joj3FIkQwVcfj1VsLV_pj6Cwcr&index=4)

- [M/EEG analysis with MNE](https://www.youtube.com/watch?v=t-twhNqgfSY)

- [Advanced fMRI analyses](https://brainiak.org/tutorials/)

- [Reaction time distributions: an interactive overview](https://lindeloev.shinyapps.io/shiny-rt/)

- [Neuroimaging and Data Science](http://neuroimaging-data-science.org/root.html)

- [Meta-Learned Models of Cognition](https://github.com/marcelbinz/Meta-Learned-Models-of-Cognition)

- [The Art and Science of Modeling Human Decision-Making](https://github.com/marcelbinz/The-Art-and-Science-of-Modeling-Human-Decision-Making)

- [NivStan](https://nivlab.github.io/nivstan/) - Recipes for cognitive modeling using Stan

- [Pillow Lab Tutorials](https://pillowlab.wordpress.com/)

- [MAPs](https://medicine.yale.edu/psychiatry/education/map/) - Methods And Primers for Computational Psychiatry and Neuroeconomics

- [Python脑电数据处理中文手册](https://github.com/ZitongLu1996/Python-EEG-Handbook)

- [Geometric constraints on human brain function](https://twitter.com/SaadJbabdi/status/1668597401299369986)



## Books



- [Theoretical Neuroscience](http://www.gatsby.ucl.ac.uk/~dayan/book/)

- [Bayesian models of perception and action](https://www.cns.nyu.edu/malab/bayesianbook.html)

- [Theoretical Modeling for cognitive science and psychology](https://computationalcognitivescience.github.io/lovelace/home)

- [Algorithms for Decision Making](https://algorithmsbook.com/)

- [Mathematics for Machine Learning](https://mml-book.github.io/)

- [Causal Inference: What If](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)

- [Statistical Mechanics of Neural Networks](https://www.labxing.com/lab/666/news/677)

- [Modeling Neural Circuits Made Simple](https://github.com/RobertRosenbaum/ModelingNeuralCircuits)

- [Network Science](http://networksciencebook.com/)

- [Introduction to Data Science](http://rafalab.dfci.harvard.edu/dsbook/)

- [An Introduction to Statistical Learning](https://www.statlearning.com/)

  - [ISLR tidymodels labs](https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/index.html)

- [Probabilistic Machine Learning](https://probml.github.io/pml-book/)

- [Patterns, Predictions, and Actions](https://mlstory.org/)

- [Bayesian Data Analysis](http://www.stat.columbia.edu/~gelman/book/)

- [Almost None of the Theory of Stochastic Processes](https://www.stat.cmu.edu/~cshalizi/almost-none/)

- [Bayesian Data Analysis using Probabilistic Programs](https://mhtess.github.io/bdappl/)

- [Introduction to Modern Statistics](https://openintro-ims.netlify.app/index.html)

- [Applied Causal Analysis](https://bookdown.org/paul/applied-causal-analysis/)

- [Probabilistic language understanding](http://www.problang.org/)

- [Modeling Agents with Probabilistic Programs](https://agentmodels.org/)



## Open Data



- [OpenData](https://nivlab.github.io/opendata/) - A collection of publicly available behavioral datasets

- [THINGS](https://things-initiative.org/): Behavioral and neuroscience data in object recognition and understanding.

- [SenseLab](https://senselab.med.yale.edu/) - The SenseLab Project is a long-term effort to build integrated, multidisciplinary models of neurons and neural systems.

- [CRCNS](https://crcns.org/data-sets) - Collaborative Research in Computational Neuroscience: Data sharing

- [Natural Scenes Dataset](http://naturalscenesdataset.org/): a large-scale fMRI dataset

- [Open dataset of theory of mind reasoning in early to middle childhood](https://psyarxiv.com/gczp9/)

- [Moral Machine](https://osf.io/3hvt2/)

- [Google Dataset Search](https://datasetsearch.research.google.com/)

- [Human Connectom](https://www.humanconnectome.org/)

- [Open Neuro](https://openneuro.org/)

- [Open fMRI](https://openfmri.org/)

- [NCBI](https://www.ncbi.nlm.nih.gov/)

- [UK BioBank](https://www.ukbiobank.ac.uk/)



## Podcast



- [Lex Fridman Podcast](https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4)

- [Brain Inspired](https://braininspired.co/)

- [TalkRL](https://www.talkrl.com/)

- [dataskeptic](http://dataskeptic.com/)