Transparent cognitive sandbox disguised as a Tamagotchi-style digital pet - watch brains grow & rewire through Hebbian learning & Neurogenesis

NGC-Learn: Computational Neuroscience and NeuroAI in Python

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

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

Brain-inspired knowledge graph: spreading activation, Hebbian learning, memory consolidation.

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

Code for paper NeurIPS AMHN 2023

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

A Computational Substrate for Self-Organizing Biologically-Plausible AI

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.

Contrastive Hebbian learning on MNIST, reaching ~97% accuracy with a small 784-128-10 MLP.

Malleable spiking neural network framework and training platform.

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

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

In this project, I used Hebbian, Perceptron and Adaline neural networks to implement AND gate, and OR gate.

A persistent epistemic substrate for AI. Nous treats language models as larynx, not mind, and benchmarks epistemic structure instead of output fluency.

tensorflow-engram: A Python package for Engram Neural Networks, adding biologically-inspired Hebbian memory and engram layers to TensorFlow/Keras models, supporting memory traces, plasticity, attention, and sparsity for neural sequence learning.