Transparent cognitive sandbox disguised as a Tamagotchi-style digital pet - watch brains grow & rewire through Hebbian learning & Neurogenesis
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Updated
Jun 11, 2026 - Python
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
Persistent memory + local AI for coding agents. 1.7B–32B open-weight LLM fleet, cross-session Mind Palace, cognitive routing, L3 grounding verifier, multi-agent Hivemind. Works with Claude Code, Cursor, VS Code. Offline-first, HIPAA-ready. Free tier included.
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.
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
NeuroMorphic Predictive Model with Spiking Neural Networks (SNN) using Pytorch
Non-bijunctive attention collapse for LLM inference — POWER8 hardware AES (vcipher) + AltiVec vec_perm. Hebbian path selection, cross-head diffusion, O(1) KV prefiltering.
Brain-inspired knowledge graph: spreading activation, Hebbian learning, memory consolidation.
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.
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.
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
Implementation of Hopfield Neural Network in Python based on Hebbian Learning Algorithm
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