BioTorch is a PyTorch framework specializing in biologically plausible learning algorithms
The Hierarchical Intrinsically Motivated Agent (HIMA) is an algorithm that is intended to exhibit an adaptive goal-directed behavior using neurophysiological models of the neocortex, basal ganglia, and thalamus.
PyTorch implementation of the paper "Spatio-Temporal Decoupled Learning for Spiking Neural Networks"
🐣 Code for my master thesis "Biologically Plausible Deep Learning through Neuroevolution"
PyTorch implementation of the paper "Scaling Supervised Local Learning with Augmented Auxiliary Networks"
Awesome list of research publications and media on biologically-motivated learning algorithms.
Predictive Coding with errors, not states
Pytorch Implementation, Experiments and Exploration of Biologically Plausible Neural Networks as a final project of CLPS1291 @ Brown
Replicating the results of a paper on underlying mechanisms of feedback alignment .
This repo is the implementation of perturbation based algorithms for training neural networks - we evaluate the scalability of node perturbation with network width and depth on modern datasets such as MNIST, CIFAR, etc.
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