# 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

```bash
pip install -e ".[dev]"
```

For GPU support:
```bash
pip install -e ".[dev,gpu]"
```

## Quick Start

```python
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
```