computational-neuroscience
There are 412 repositories under computational-neuroscience topic.
brian-team/brian2
Brian is a free, open source simulator for spiking neural networks.
eselkin/awesome-computational-neuroscience
A list of schools and researchers in computational neuroscience
asoplata/open-computational-neuroscience-resources
A publicly-editable collection of open computational neuroscience resources
csinva/csinva.github.io
Slides, paper notes, class notes, blog posts, and research on ML ๐, statistics ๐, and AI ๐ค.
BrainCog-X/Brain-Cog
Brain-inspired Cognitive Intelligence Engine (BrainCog) is a brain-inspired spiking neural network based platform for Brain-inspired Artificial Intelligence and simulating brains at multiple scales. The long term goal of BrainCog is to provide a comprehensive theory and system to decode the mechanisms and principles of human intelligence and its evolution, and develop artificial brains for brain-inspired conscious living AI in future Human-AI symbiotic Society.
neurolib-dev/neurolib
Easy whole-brain modeling for computational neuroscientists ๐ง ๐ป๐ฉ๐ฟโ๐ฌ
jiaxiaogang/he4o
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CompCogNeuro/book
Computational Cognitive Neuroscience textbook
genn-team/genn
GeNN is a GPU-enhanced Neuronal Network simulation environment based on code generation for Nvidia CUDA.
BlueBrain/BluePyOpt
Blue Brain Python Optimisation Library
RatInABox-Lab/RatInABox
A python package for simulating movement and spatial cell types (e.g. place cells, grid cells) in continuous environments.
mne-tools/mne-cpp
MNE-CPP: A Framework for Electrophysiology
jiaxiaogang/HELIX_THEORY
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computational-neuroscience/Computational-Neuroscience-UW
Python scripts that supplement the Coursera Computational Neuroscience course by the University of Washington
NACLab/ngc-learn
NGC-Learn: Neurobiological Learning and Biomimetic Systems Simulation in Python
asoplata/open-science-resources
A publicly-editable collection of open science resources, including tools, datasets, meta-resources, etc.
mrkrd/cochlea
Inner ear models for Python
fzenke/auryn
Auryn: A fast simulator for spiking neural networks with synaptic plasticity
NSBLab/BrainEigenmodes
Code supporting 'Geometric constraints on human brain function'
jonescompneurolab/hnn
The Human Neocortical Neurosolver (HNN) is a software tool that gives researchers/clinicians the ability to develop/test hypotheses on circuit mechanisms underlying EEG/MEG data.
alfredcai/Coursera-Computational-NeuroScience
Coursera Computational-NeuroScience course of the University of Washington
emer/leabra
Go implementation of Leabra algorithm for biologically-based models of cognition, based on emergent framework (with Python interface)
gifale95/eeg_encoding
Use DNNs to build encoding models of EEG visual responses.
btel/python-in-neuroscience-tutorials
Collection of tutorials about methods of computational neuroscience using Python
INM-6/multi-area-model
A large-scale spiking model of the vision-related areas of macaque cortex.
BlueBrain/eFEL
Electrophys Feature Extraction Library
Devrim-Celik/interactive_neuron_model_simulator
Interactive Matplotlib Plots in Python, convering Models such as the Leaky Integrate and Fire, Izhikevich Model, FitzHugh-Nagumo Model etc...
paninski-lab/yass
YASS: Yet Another Spike Sorter
synergetics/spectrum
Higher Order Spectrum Estimation toolkit
brian-team/brian2cuda
A brian2 extension to simulate spiking neural networks on GPUs
mschrimpf/neural-nlp
[PNAS'21] The neural architecture of language: Integrative modeling converges on predictive processing
XBTinChina/CCN_Association
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metr0jw/Spiking-Neural-Network-on-FPGA
Leaky Integrate and Fire (LIF) model implementation for FPGA
esi-neuroscience/syncopy
Systems Neuroscience Computing in Python: user-friendly analysis of large-scale electrophysiology data
rougier/CNCC-2020
Computational Neuroscience Crash Course (University of Bordeaux, 2020)
ContextLab/computational-neuroscience
Short undergraduate course taught at University of Pennsylvania on computational and theoretical neuroscience. Provides an introduction to programming in MATLAB, single-neuron models, ion channel models, basic neural networks, and neural decoding.