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

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

A lightweight and flexible framework for Hebbian learning in PyTorch.

Hopfield network implemented with Python

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

Studying collective memories of internet users using Wikipedia viewership statistics

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

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

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

unsupervised learning of natural images -- à la SparseNet.

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

Computational Neuroscience projects.

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.

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

In this project, I used Hebbian, Perceptron, Adaline, MultiClassPerceptron and MultiClassAdaline neural networks to implement X and O character recognition.

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

Projects of the course Artificial Neural Networks by Dr. Mozayani - Fall 2019