Meta-Learning through Hebbian Plasticity in Random Networks: https://arxiv.org/abs/2007.02686

NGC-Learn: Neurobiological Learning and Biomimetic Systems Simulation in Python

Hopfield network implemented with Python

A lightweight and flexible framework for Hebbian learning in PyTorch.

Python implementation of the Epigenetic Robotic Architecture (ERA). It includes standalone classes for Self-Organizing Maps (SOM) and Hebbian Networks.

PyPi Package of Self-Organizing Recurrent Neural Networks (SORN) and Neuro-robotics using OpenAI Gym

NeuroMorphic Predictive Model with Spiking Neural Networks (SNN) using Pytorch

Studying collective memories of internet users using Wikipedia viewership statistics

This repository has implementations of various alternatives to backpropagation for training neural networks.

Code for the assignments for the Computational Neuroscience Course BT6270 in the Fall 2018 semester

Code for Limbacher, T. and Legenstein, R. (2020). H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

Implementation of Hopfield Neural Network in Python based on Hebbian Learning Algorithm

Computational Neuroscience projects.

unsupervised learning of natural images -- à la SparseNet.

A neural network model builder, leveraging a neuro-symbolic interface.

🐣 Code for my master thesis "Biologically Plausible Deep Learning through Neuroevolution"

This is a python implementation of Kuramoto model with adaptive rewiring. Adaptive rewiring is meant to simulate the ebb and flow of social interactions.

NeurIPS AMHN 2023: Neural recordings artifacts preprocessing model and pipeline. Hopfield Deep Neural Networks for Artifact preprocessing in Brain State Decoding

Malleable spiking neural network framework and training platform.

Code for Limbacher, T., Özdenizci, O., & Legenstein, R. (2022). Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity. arXiv preprint arXiv:2205.11276.