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awesome-computational-neuroscience

An awesome list of computational neuroscience and computational cognitive science.
https://github.com/sakimarquis/awesome-computational-neuroscience

Last synced: about 15 hours ago
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Course
- Computational Cognitive Science
  - Advancing AI through cognitive science - NYU PSYCH-GA 3405.001 / DS-GA 3001.014, Brenden Lake, Spring 2019
  - Modeling the Mind
  - 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 Neuroscience - UC Davis, Randall O'Reilly, Spring 2020
  - Bayesian Statistics and Hierarchical Bayesian Modeling - Lei Zhang
  - Modeling the Mind
- Computational Neuroscience
  - 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
  - Dynamical Systems in Neuroscience - NeuroLogos
  - Introduction to Neural Computation - MIT 9.40, Michale Fee, Spring 2018
  - Understanding Vision: theory, models, and data - Li Zhaoping
  - Computational Neuroscience - SYDE 552, Terry Stewart, Winter 2021
- Neuroscience
  - The Human Brain - MIT 9.11, Nancy Kanwisher, Spring 2018
  - Neuroscience and Neuroimaging - Coursera
  - Intro to fMRI class - Rajeev Raizada
  - fMRI Bootcamp
  - 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
  - NIH fMRI Courses
  - NIH fMRI Courses
  - The Human Brain - MIT 9.11, Nancy Kanwisher, Spring 2018
  - Intro to fMRI class - Rajeev Raizada
  - Human Behavioral Biology - Stanford, Robert Sapolsky
  - Neural Data Science - Tübingen, Philipp Berens, 2021
  - Introduction to Brain and Consciousness
  - Grossbergian Neuroscience - NeuroLogos
  - Introduction to ERPs - Steve Luck
- Psychology
  - Philosophical Psychology - UMN, Paul Meehl, Winter 1989
  - Computational Reason - NeuroLogos
  - Computational Reason - NeuroLogos
Package
- Computational Neuroscience
  - 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.
- Computational Cognitive Science
  - Computational and Behavioral Modeling - CBM provides tools for hierarchical Bayesian inference
  - 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.
  - HDDM - A python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC).
  - rlssm - A Python package for fitting reinforcement learning models, sequential sampling models, and combinations of the two, using Bayesian parameter estimation.
- Machine Learning
  - 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
  - Tianshou - A reinforcement learning platform based on pure PyTorch.
Talk
- Machine Learning
Summer School
- Machine Learning
Tutorial
- Machine Learning
  - Online Resources for Systems and Computational Neuroscience
  - MIT fMRI Bootcamp
  - Reproducible Data Analysis in Jupyter - Jake Vanderplas
  - DartBrains
  - RBootcamp
  - Data Science - Trenton McKinney
  - Learning Machine - RenChu Wang
  - Models of Learning - Hanneke den Ouden
  - PyTorch - Python Deep Learning - deeplizard
  - BrainIAK Tutorial
  - Python and Matlab programs for fMRI
  - RSA Workshop
  - 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
  - Probabilistic Models of Cognition - Noah Goodman & Joshua Tenenbaum
  - Recurrent neural networks for cognitive neuroscience - 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
  - Spinning Up in Deep RL - OpenAI
  - Linear Algebra for Theoretical Neuroscience - Ken Miller
  - Modeling in Neuroscience - Gunnar Blohm
  - Computational and Inferential Thinking: The Foundations of Data Science
  - Kalman Filter Tutorial
  - Bayesian Deep Learning and Probabilistic Model Construction
  - Data Skills for Neuroscientists - SfN
  - Statistical tools for high-throughput data analysis
  - Statistical models for neural data
  - M/EEG analysis with MNE
  - Advanced fMRI analyses
  - Reaction time distributions: an interactive overview
  - Neuroimaging and Data Science
  - Pillow Lab Tutorials
  - MAPs - Methods And Primers for Computational Psychiatry and Neuroeconomics
  - Geometric constraints on human brain function
  - Andy’s Brain Book
  - Bayesian Model - Rasmus Bååth
  - MIT fMRI Bootcamp
  - Reproducible Data Analysis in Jupyter - Jake Vanderplas
  - PyTorch - Python Deep Learning - deeplizard
  - Spiking Neural Networks Tutorial - Dan Goodman
  - Better Markov Chain
Books
- Machine Learning
Open Data
- Machine Learning
  - THINGS
  - 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
  - Natural Scenes Dataset - scale fMRI dataset
  - Open dataset of theory of mind reasoning in early to middle childhood
  - Moral Machine
  - Google Dataset Search
  - Human Connectom
  - Open Neuro
  - Open fMRI
  - NCBI
  - UK BioBank
Podcast
- Machine Learning

Programming Languages

Python 3 MATLAB 2 JavaScript 1 Jupyter Notebook 1 Matlab 1 HTML 1

Categories

Tutorial 49 Course 49 Talk 19 Books 18 Open Data 12 Package 12 Summer School 4 Podcast 3

Sub Categories

Machine Learning 108 Neuroscience 20 Computational Neuroscience 19 Computational Cognitive Science 16 Psychology 3

Keywords

matlab 2 brain-dynamics-modeling 1 brain-inspired-computing 1 brain-simulations 1 brainpy 1 spiking-neural-networks 1 bayesian-statistics 1 computational-modeling 1 computational-neuroscience 1 computational-psychiatry 1 jspsych 1 web-experiments 1 bayesian-inference 1 data-analysis 1 gaussian-processes 1 machine-learning 1 variational-inference 1 bayesian-optimization 1 log-likelihood 1 noiseless-functions 1 noisy-functions 1 optimization-algorithms 1