/Brain-Cog

Brain-inspired Cognitive Intelligence Engine (BrainCog) is a brain-inspired spiking neural network based platform for simulating the cognitive brains of different animal species at multiple scales and realizing brain-inspired Artificial Intelligence. 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 machines in future human-machine society.

Primary LanguagePython

braincog

braincog is an open source spiking neural network based brain-inspired cognitive intelligence engine for Brain-inspired Artificial Intelligence and brain simulation. More information on braincog can be found on its homepage http://www.brain-cog.network/

If you use braincog in your research, the following paper can be cited as the source for braincog.

Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, Zhuoya Zhao, Hongjian Fang, Yuwei Wang, Yang Li, Xin Liu, Chengcheng Du, Qingqun Kong, Zizhe Ruan, Weida Bi. braincog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation. arXiv:2207.08533, 2022. https://arxiv.org/abs/2207.08533

./figures/logo.png

braincog provides essential and fundamental components to model biological and artificial intelligence.

image

Brain-Inspired AI

braincog currently provides cognitive functions components that can be classified into five categories:

  • Perception and Learning
  • Decision Making
  • Motor Control
  • Knowledge Representation and Reasoning
  • Social Cognition

mtmt

Brain Simulation

braincog currently include two parts for brain simulation:

  • Brain Cognitive Function Simulation
  • Multi-scale Brain Structure Simulation

bmbm10s bm10sbh10s

The anatomical and imaging data is used to support our simulation from various aspects.

Requirements:

  • CUDA toolkit == 11.
  • numpy >= 1.21.2
  • scipy >= 1.8.0
  • h5py >= 3.6.0
  • torch >= 1.10
  • torchvision >= 0.12.0
  • torchaudio >= 0.11.0
  • timm >= 0.5.4
  • matplotlib >= 3.5.1
  • einops >= 0.4.1
  • thop >= 0.0.31
  • pyyaml >= 6.0
  • loris >= 0.5.3
  • pandas >= 1.4.2
  • tonic (special)
  • pandas >= 1.4.2
  • xlrd == 1.2.0

Install

# optional, if use datasets 
git clone https://github.com/FloyedShen/tonic.git
cd tonic 
pip install -e .

or

pip install git+https://github.com/FloyedShen/tonic.git

# To install braincog
pip install braincog

or

git clone https://github.com/braincog-X/Brain-Cog.git
cd braincog
pip install -e .

or

pip install git+https://github.com/braincog-X/Brain-Cog.git

Example

  1. Examples for Image Classification
cd ./examples/Perception_and_Learning/img_cls/bp 
python main.py --model cifar_convnet --dataset cifar10 --node-type LIFNode --step 8 --device 0
  1. Examples for Event Classification
cd ./examples/Perception_and_Learning/img_cls/bp 
python main.py --model dvs_convnet --node-type LIFNode --dataset dvsc10 --step 10 --batch-size 128 --act-fun QGateGrad --device 0

Other braincog features and tutorials can be found at http://www.brain-cog.network/docs/