neuroscience-inspired-ai
There are 15 repositories under neuroscience-inspired-ai topic.
MinghuiChen43/awesome-deep-phenomena
A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...
htm-community/htm.core
Actively developed Hierarchical Temporal Memory (HTM) community fork (continuation) of NuPIC. Implementation for C++ and Python
SelfishGene/neuron_as_deep_net
Code behind the work "Single Cortical Neurons as Deep Artificial Neural Networks", published in Neuron 2021
neuroailab/convrnns
ConvRNN Model Zoo: ImageNet pre-trained convolutional recurrent neural networks
doerlbh/mentalRL
Code for our AAMAS 2020 paper: "A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry".
emer/axon
Axon is a spiking, biologically-based neural model driven by predictive error-driven learning, for systems-level models of the brain
Pervasive-AI-Lab/sparse-dynamic-synapses
"The Unreasonable Effectiveness of Sparse Dynamic Synapses for Continual Learning" paper project.
RoozbehRazavi/BIMRL
Implementation of BIMRL: Brain Inspired Meta Reinforcement Learning - Roozbeh Razavi et al. (IROS 2022)
SelfishGene/filter_and_fire_neuron
Code behind the work "Multiple Synaptic Contacts Combined with Dendritic Filtering Enhance Spatio-Temporal Pattern Recognition of Single Neurons", bioRxiv 2022
neuroailab/mouse-vision
Models of Mouse Vision: Self-supervised pre-trained networks and training code (PyTorch)
BKHMSI/alchemist
Official Repository of the "How to Learn and Represent Abstractions: An Investigation using Symbolic Alchemy" Paper
neurogadgets/Syntheta
Artificial Biological Intelligence
LiveBacteria/QNNF-Theory-Integration
Quantum neural network research implementing multi-dimensional neuron representations. Explores theoretical integration of quantum computing principles into neural systems to investigate emergent cognition and consciousness.
antodima/compneuro
Computational Neuroscience models
personx000/ktree
Polished code for "Can a single neuron solve MNIST? The computational power of biological dendritic trees”