Transparent cognitive sandbox: Raise digital squids - watch brains grow & rewire themselves through Hebbian learning & Neurogenesis
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Updated
Mar 28, 2026 - Python
Transparent cognitive sandbox: Raise digital squids - watch brains grow & rewire themselves through Hebbian learning & Neurogenesis
NGC-Learn: Computational Neuroscience Simulation and NeuroAI Design 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
NeuroMorphic Predictive Model with Spiking Neural Networks (SNN) using Pytorch
Studying collective memories of internet users using Wikipedia viewership statistics
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
Persistent memory MCP server for AI agents — Rust, 13 tools, knowledge graph, Hebbian learning, exponential decay, hybrid search (RRF + pgvector), anti-hallucination grounding. Zero tech debt.
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
Non-bijunctive attention collapse for LLM inference — POWER8 hardware AES (vcipher) + AltiVec vec_perm. Hebbian path selection, cross-head diffusion, O(1) KV prefiltering.
unsupervised learning of natural images -- à la SparseNet.
Muscle memory for Claude, OpenClaw, and AI agents. Zero-cost Hebbian memory system — learns which files matter through co-access patterns, predicts what you need next.
Contrastive Hebbian learning on MNIST, reaching ~97% accuracy with a small 784-128-10 MLP.
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