NeuroGraph

Bio-inspired energy-gradient deep learning framework.

Overview

NeuroGraph explores training paradigms beyond backpropagation:

• Energy-based learning: Networks relax to energy minima instead of computing analytic gradients
• Reward-modulated plasticity: Three-factor learning rules with neuromodulator signals
• Autonomous pruning: Self-organizing network topology through structural plasticity
• Architecture search: Differentiable graph exploration for optimal connectivity

Setup

pip install -e ".[dev]"

For GPU support:

pip install -e ".[dev,gpu]"

Quick Start

from neurograph.core.energy import compute_energy, EnergyConfig

config = EnergyConfig(data_weight=1.0, reg_weight=0.01)
energy = compute_energy(params, activities, inputs, targets, config=config)

Project Structure

src/neurograph/
├── core/          # Energy functions, equilibrium dynamics, neurons
├── learning/      # EqProp, reward modulation, Hebbian rules
├── pruning/       # Magnitude/activity-based pruning, structural plasticity
├── architecture/  # Graph NAS, topology mutation
├── env/           # Gymnasium wrappers
└── utils/         # Visualization, logging, metrics