A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...

Actively developed Hierarchical Temporal Memory (HTM) community fork (continuation) of NuPIC. Implementation for C++ and Python

Code behind the work "Single Cortical Neurons as Deep Artificial Neural Networks", published in Neuron 2021

ConvRNN Model Zoo: ImageNet pre-trained convolutional recurrent neural networks

Code for our AAMAS 2020 paper: "A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry".

Axon is a spiking, biologically-based neural model driven by predictive error-driven learning, for systems-level models of the brain

"The Unreasonable Effectiveness of Sparse Dynamic Synapses for Continual Learning" paper project.

Implementation of BIMRL: Brain Inspired Meta Reinforcement Learning - Roozbeh Razavi et al. (IROS 2022)

Code behind the work "Multiple Synaptic Contacts Combined with Dendritic Filtering Enhance Spatio-Temporal Pattern Recognition of Single Neurons", bioRxiv 2022

Models of Mouse Vision: Self-supervised pre-trained networks and training code (PyTorch)

Official Repository of the "How to Learn and Represent Abstractions: An Investigation using Symbolic Alchemy" Paper

Artificial Biological Intelligence

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.

Polished code for "Can a single neuron solve MNIST? The computational power of biological dendritic trees”

Computational Neuroscience models