hebbian-learning

Raise a neural network as a pet - a transparent cognitive sandbox where a digital squid learns, grows, and rewires itself via 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

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.

Most AI agents forget you the moment the tab closes. Constellation Engine gives them a hippocampus — a living star map with spreading activation, Hebbian writeback, episodic recall, and post-turn consolidation. Local-first, model-agnostic, AGPL.

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

Non-bijunctive attention collapse for LLM inference — POWER8 hardware AES (vcipher) + AltiVec vec_perm. Hebbian path selection, cross-head diffusion, O(1) KV prefiltering.

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.

Persistent memory MCP server for AI agents — Rust, 19 tools, knowledge graph, Hebbian learning, episodic memory, contradiction detection, prospective triggers, Bayesian calibration, zero-config Docker setup.

Code and paper reproductions for MLPs Are Hebbians

Persistent memory for AI coding agents. Your agent learns your codebase the way a senior engineer would — what files go together, what you usually edit next. Works with Claude Code, Cursor, Cline, Continue. 100% local. (Side effect: 12-50× cheaper code questions.)

Biologically inspired language model using Jaccard Surprise as its only training signal. No backprop. No GPU. Online Hebbian learning from corrections. Two-layer cortex with apical feedback. Runs on CPU under 200MB.

Agent memory for LLM agents: 7 neuroscience-inspired layers (working, episodic, semantic, procedural) with FSRS spaced repetition and memory consolidation. Zero-dependency TypeScript library, Model Context Protocol (MCP) server, Vercel AI SDK middleware. Wins 9/9 answer-quality comparisons on LongMemEval-500 (3 judges, Bonferroni).

Code for paper NeurIPS AMHN 2023

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