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.
"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."
Table of contents generated with markdown-toc
- Computational Neuroscience - Coursera
- Computational Neuroscience: the basics - INCF
- Cajal Course Computational Neuroscience - INCF
- Neuronal Dynamics - EPFL, Wulfram Gerstner
- Introduction to Neural Computation - MIT 9.40, Michale Fee, Spring 2018
- Neuromatch Academy - Computational Neuroscience
- Understanding Vision: theory, models, and data - Li Zhaoping
- Computational Neuroscience - SYDE 552, Terry Stewart, Winter 2021
- Dynamical Systems in Neuroscience - NeuroLogos
- The biophysical basis of neurons and networks - UCSD Physics 178/278, David Kleinfeld
- Neural Computation - UCB VS265, Bruno Olshausen
- Gatsby Unit Course Materials
- Modeling the Mind, UA NSCS 344, Robert Wilson, 2020
- How to build a brain from scratch - UCL, Chris Summerfield
- Beginners guide to doing experimental cognitive science research - Todd Gureckis
- Computational Psychiatry - ETH Zurich, Autumn 2021
- Computational Cognitive Neuroscience - UC Davis, Randall O'Reilly, Spring 2020
- Bayesian Statistics and Hierarchical Bayesian Modeling - Lei Zhang
- Computational cognitive modeling - NYU PSYCH-GA 3405.004 / DS-GA 1016.003, Brenden Lake, Spring 2023
- Advancing AI through cognitive science - NYU PSYCH-GA 3405.001 / DS-GA 3001.014, Brenden Lake, Spring 2019
Intro:
- Learning From Data - Caltech CS156, Yaser Abu-Mostafa, 2012
- Artificial Intelligence - MIT 6.034, Patrick Winston, Fall 2010
- Introduction to Machine Learning - CMU 10-701/15-781, Barnabas Poczos, Spring 2013
- Foundations of Machine Learning - David S. Rosenberg
- Machine Learning - DS-GA 1003, Julia Kempe, Spring 2019
- Mathematical Tools for Data Science - NYU DS, Carlos Fernandez-Granda
- Introduction to Artificial Intelligence - UCB CS 188, Fall 2022
- Machine Learning - UBC, Nando de Freitas, 2013.
- Statistical Learning - edX
- Statistical Learning Theory and Applications - MIT 9.520/6.860, 2019
- mlcourse.ai - Yury Kashnitsky
- Data Science in Practice - UCSD COGS 108
- Practical Data Science - Pat Virtue
- Machine Learning - NTU, 李宏毅
- Bio-Inspired AI and Optimization - ASU IEE/CSE 598, Ted Pavlic, Spring 2022
Advance:
- Machine Learning with Graphs - Stanford CS224W
- Probabilistic Graphical Models - Coursera, Daphne Koller
- Probabilistic Graphical Models - CMU 10-708, Eric Xing, Spring 2019
- Probabilistic Machine Learning - Tübingen, Philipp Hennig, Summer 2020
- Mining Massive Datasets - Stanford
- Mathematics of Machine Learning Summer School - Paul G. Allen School
- Machine Learning Summer School - Tübingen, 2013
- Statistical Machine Learning - CMU, Ryan Tibshirani & Larry Wasserman, Spring 2017
- Economics, AI, and Optimization - Christian Kroer
- 机器学习-白板推导系列
- Dive into Deep Learning
- Deep Learning - Oxford, Nando de Freitas, 2015
- Practical Deep Learning - Jeremy Howard
- Convolutional Neural Networks for Visual Recognition - Stanford CS231n, Andrej Karpathy, Winter 2016
- Introduction to Deep Learning - CMU 11-785
- Deep Learning - UPenn CIS 522,2022
- Natural Language Processing with Deep Learning - Stanford CS224N, Christopher Manning
- Deep Learning - NYU DS-GA 1008, Yann LeCun & Alfredo Canziani, Spring 2021
- Neuromatch Academy Deep Learning
- Full Stack Deep Learning
Advance:
- Analyses of Deep Learning - Stanford Stats 385, Fall 2017
- Foundations of Deep Learning - UMD CMSC 828W, Soheil Feizi, Fall 2020
- Representation Learning - MILA IFT 6135
- Introduction to Reinforcement Learning - DeepMind, David Silver, 2015
- Intro to AI - UC Berkeley CS188
- Deep Multi-Task and Meta Learning - CS 330, Chelsea Finn, Fall 2022
- Deep Reinforcement Learning - UCB CS 285, Sergey Levine, Fall 2021
- Mobile Robotics: Methods and Algorithms - University of Michigan NA 568/EECS 568/ROB 530
- Advanced Robotics - UCB CS 287, Pieter Abbeel, Fall 2019
- DLRL Summer School - CIFAR, 2019
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Introduction to Probability - MIT RES.6-012, John Tsitsiklis, Spring 2018 (BEST!)
-
Statistical Rethinking - Richard McElreath
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Discrete Stochastic Processes - MIT 6.262, Robert Gallager, Spring 2011
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Information Theory, Pattern Recognition, and Neural Networks - David MacKay
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Bayesian Data Analysis course - Aki Vehtari
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Uncertain: Modern Topics in Uncertainty Estimation – CIS 7000: A Course at the University of Pennsylvania - Aaron Roth
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Convex Optimization - Stanford EE 364A, Stephen Boyd
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Optimization Algorithms - Constantine Caramanis
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Causal Diagrams - edX
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Introduction to Causal Inference - Brady Neal
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High-Dimensional Probability - UCI, Roman Vershynin
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Mini Lectures in Probability - Nassim Nicholas Taleb
- The Human Brain - MIT 9.11, Nancy Kanwisher, Spring 2018
- Neuroscience and Neuroimaging - Coursera
- Intro to fMRI class - Rajeev Raizada
- fMRI Bootcamp
- NeuroHackademy
- Human Behavioral Biology - Stanford, Robert Sapolsky
- Neural Data Science - Tübingen, Philipp Berens, 2021
- Introduction to Brain and Consciousness
- Grossbergian Neuroscience - NeuroLogos
- Dynamic Data Visualization Workshop - An NIMH-hosted workshop on principles, tools, and approaches to constructing effective dynamic data visualizations
- Introduction to ERPs - Steve Luck
- Analyzing Neural Time Series Data - Mike X Cohen
(Also see this)
- Introduction to Computer Science - Harvard CS 50
- Structure and Interpretation of Computer Programs - UCB CS 61A
- Data Structures - UCB CS 61B
- Theory of Computation - MIT 18.404J, Michael Sipser, Fall 2020
- Linear Algebra - MIT 18.06, Gilbert Strang, Spring 2005
- Matrix Methods In Data Analysis, Signal Processing, And Machine Learning - MIT 18.065, Gilbert Strang, Spring 2018
- Differential Equations - Res 18.009, Gilbert Strang & Cleve Moler, Fall 2015
- Linear Algebra Done Right - Sheldon Axler
- Introduction to Applied Linear Algebra
- Multivariable Calculus and Linear Algebra - Theodore Shifrin
- Mathematics for Computer Science - MIT 6.042J, Tom Leighton & Marten van Dijk, Fall 2010
- Introduction to University Mathematics - Oxford
- Philosophical Psychology - UMN, Paul Meehl, Winter 1989
- Computational Reason - NeuroLogos
- Dynamical Systems - Steve Brunton
- Control Theory Bootcamp - Steve Brunton
- Linear Dynamical Systems - Stanford EE263, Stephen Boyd
- Theoretical Minimum - Leonard Susskind
- Complexity Explorer - Santa Fe Institute
- Nonlinear Dynamics and Chaos - Cornell MAE5790, Steven Strogatz, Spring 2014
- Nonlinear Systems - MIT, Jean-Jacques Slotine
- BrainPy - A flexible, efficient, and extensible framework for computational neuroscience and brain-inspired computation based on the JIT compilation.
- Nengo - The Nengo Brain Maker is a Python package for building, testing, and deploying neural networks.
- 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.
- HDDM - A python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC).
- Computational and Behavioral Modeling - CBM provides tools for hierarchical Bayesian inference
- rlssm - A Python package for fitting reinforcement learning models, sequential sampling models, and combinations of the two, using Bayesian parameter estimation.
- RL_DDM - Reinforcement learning + drift-diffusion model repository.
- Bandits - Python library for Multi-Armed Bandits implements the following algorithms: Epsilon-Greedy, UCB1, Softmax, Thompson Sampling
- NivTurk - Niv lab tools for securely serving and storing data from online computational psychiatry experiments.
- Tianshou - A reinforcement learning platform based on pure PyTorch.
- Variational Bayesian Monte Carlo - VBMC is an approximate inference method designed to fit and evaluate computational models with a limited budget of potentially noisy likelihood evaluations.
- BADS - BADS is a fast hybrid Bayesian optimization algorithm designed to solve difficult optimization problems, in particular related to fitting computational models
- Ray - A unified framework for scaling AI and Python applications.
- Weights & Biases - Machine learning platform for developers to build better models faster.
- MIT CBMM
- Cosyne
- CCN
- CCN 2017
- Harvard Machine Learning Foundations Group
- Theoretical Neuroscience
- CogSci
- Simons Institute
- UCL NeuroAI
- RTG Computational Cognition
- MRC Cognition and Brain Sciences Unit
- Spiking Neural networks as Universal Function Approximators -
- MBL Brains, Minds and Machines
- NeurIPS 2022
- Meaning of Life Symposium
- Dynamic Field Theory
- SFN Annual Meeting
- Marine Biological Laboratory
- Cold Spring Harbor Asia
- Graduate Workshop in Computational Social Science at Santa Fe Institute
Other tutorials see Online Resources for Systems and Computational Neuroscience
Intro:
- Python Data Science - Jake VanderPlas
- Reproducible Data Analysis in Jupyter - Jake Vanderplas
- DartBrains
- RBootcamp
- Data Science - Trenton McKinney
- Learning Machine - RenChu Wang
- Models of Learning - Hanneke den Ouden
- Bayesian Model - Rasmus Bååth
- PyTorch - Python Deep Learning - deeplizard
- Andy’s Brain Book
- BrainIAK Tutorial
- Python and Matlab programs for fMRI
- RSA Workshop
- Quantitative Neuroscience
- Python for the practicing neuroscientist
- The Multi-Armed Bandit Problem and Its Solutions
- AI Wiki
- Machine Learning Mastery
- Essential Math for Data Science
- What does MEG measure? 😭
- Learn Shell
- Interactive Vim tutorial
Advance:
- Probabilistic Models of Cognition - Noah Goodman & Joshua Tenenbaum
- Recurrent neural networks for cognitive neuroscience - Guangyu Yang
- Artificial neural networks for neuroscientists - Guangyu Yang
- Recurrent Neural Network Tutorial - Kanaka Rajan
- Spiking Neural Networks Tutorial - Dan Goodman
- Modeling reinforcement learning - Maarten Speekenbrink
- Introduction to Neural Network Models of Cognition - Pablo Caceres
- Computational Models of Human Social Behavior and Neuroscience - Shawn A Rhoads
- Spinning Up in Deep RL - OpenAI
- Linear Algebra for Theoretical Neuroscience - Ken Miller
- Modeling in Neuroscience - Gunnar Blohm
- Data Skills for Neuroscientists - SfN
- Statistical tools for high-throughput data analysis
- Computational and Inferential Thinking: The Foundations of Data Science
- Kalman Filter Tutorial
- Bayesian Deep Learning and Probabilistic Model Construction
- Deep Reinforcement Learning with Pytorch
- Basic Examples for Reinforcement Learning
- Statistical models for neural data
- M/EEG analysis with MNE
- Neuroimaging and Data Science
- Meta-Learned Models of Cognition
- The Art and Science of Modeling Human Decision-Making
- NivStan - Recipes for cognitive modeling using Stan
- Pillow Lab Tutorials
- The Good Research Code Handbook
- Low-rank RNNs in ten minutes
- Deep Learning Tuning Playbook
- Theoretical Neuroscience
- Bayesian models of perception and action
- Theoretical Modeling for cognitive science and psychology
- Algorithms for Decision Making
- Mathematics for Machine Learning
- Causal Inference: What If
- Statistical Mechanics of Neural Networks
- Modeling Neural Circuits Made Simple
- Network Science
- Introduction to Data Science
- An Introduction to Statistical Learning
- Probabilistic Machine Learning
- Patterns, Predictions, and Actions
- Bayesian Data Analysis
- Almost None of the Theory of Stochastic Processes
- Bayesian Data Analysis using Probabilistic Programs
- Introduction to Modern Statistics
- Applied Causal Analysis
- Probabilistic language understanding
- Modeling Agents with Probabilistic Programs
- OpenData - A collection of publicly available behavioral datasets
- SenseLab - The SenseLab Project is a long-term effort to build integrated, multidisciplinary models of neurons and neural systems.
- CRCNS - Collaborative Research in Computational Neuroscience: Data sharing
- Google Dataset Search
- Human Connectom
- Open Neuro
- Open fMRI
- NCBI
- UK BioBank
- Everything is Connected 😭
- Deepmind
- Distill
- colah's.blog
- LessWrong
- Hessam Akhlaghpour, Author at Life Is Computation
- Machine Learning Rumination
- Sorta Insightful
- Paul Graham
- Severely Theoretical | Computational neuroscience and machine learning
- Natural Rationality
- Bradley C. Love
- neurosopher
- Mark Allen Thornton
- Talking Brains
- nikokriegeskorte – open brain science
- Statistical Modeling, Causal Inference, and Social Science
- Neuroscience, stats, and coding – A blog by Jonas Kristoffer Lindeløv
- Neuroskeptic
- Mind Hacks – Neuroscience and psychology news and views
- practiCal fMRI: the nuts & bolts
- Causal Analysis in Theory and Practice
- neuroecology | social neuroscience, decision-making, ecology, economics: thoughts from adam j calhoun
- Probably Overthinking It – A blog by Allen Downey
- Aaron Swartz
- Andrej Karpathy blog
- Denny's Blog
- BrainFacts
- Peter Norvig
- Mind Matters - Scientific American
- Joel on Software
- R-bloggers
- Edwin Chen's Blog
- Math ∩ Programming
- Bayes, pragmatics, evolution etc.
- Jonathan Weisberg
- Δ ℚuantitative √ourney
- The 20% Statistician
- Bayesian Spectacles | Powered by JASP
- Lil'Log
- Blog – xcorr: AI & neuro
- Endless computations most beautiful | A blog on neuroscience and evolution
- Clean Coder Blog