neuroptimiser 1.0.4
A modular and asynchronous framework for neuromorphic optimisation using spike-driven metaheuristics and heuristic-controlled spiking dynamics.
Requires Python: <3.11,>=3.10
Keywords: neuromorphic, spiking, optimisation, lava-nc

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# NeurOptimiser

**NeurOptimiser** is a neuromorphic optimisation framework in which metaheuristic search emerges from asynchronous spiking dynamics. It defines optimisation as a decentralised process executed by interconnected Neuromorphic Heuristic Units (NHUs), each embedding a spiking neuron model and a spike-triggered heuristic rule.

This framework enables fully event-driven, low-power optimisation by integrating spiking computation with local heuristic adaptation. It supports multiple neuron models, perturbation operators, and network topologies.

---

## ✨ Key Features

- Modular and extensible architecture using **Intel’s Lava**.
- Supports **linear** and **Izhikevich** neuron dynamics.
- Implements **random**, **fixed**, **directional**, and **Differential Evolution** operators as spike-triggered perturbations.
- Includes asynchronous **neighbourhood management**, **tensor contraction layers**, and **greedy selectors**.
- Compatible with **BBOB (COCO)** suite.
- Designed for **scalability**, **reusability**, and **future deployment** on **Loihi-class neuromorphic hardware**.

---

## 📖 Documentation
For detailed documentation, examples, and API reference, please visit the [Neuroptimiser Documentation](https://neuroptimiser.github.io/).

## 📦 Installation

### Requirements
- **Python 3.10** (tested and recommended version)
- Lava-NC environment configured

### Install with pip
```bash
# After cloning the repository and navigating to the project directory
pip install -e .
# or just install from PyPI
pip install neuroptimiser
```

### Install with uv
```bash
# After cloning the repository and navigating to the project directory
uv pip install -e .
# or just install from PyPI
uv pip install neuroptimiser
```

You can also use the provided `Makefile` for additional installation options and commands.

### Known Issues

**"Too many open files" error**: On some systems, you may encounter this error during execution. To fix it:

**Unix/Linux/macOS:**
```bash
ulimit -n 65536
```

**Windows (PowerShell):**
```powershell
# No direct equivalent - typically not needed on Windows
# If issues persist, check system file handle limits via registry
```

**Windows (Command Prompt):**
```cmd
REM Windows typically has higher default limits
REM If needed, adjust via Registry Editor or contact system administrator
```


## 🚀 Example Usage
```python
from neuroptimiser import NeurOptimiser
import numpy as np

problem_function    = lambda x: np.linalg.norm(x)
problem_bounds      = np.array([[-5.0, 5.0], [-5.0, 5.0]])

optimiser = NeurOptimiser()

optimiser.solve(
    obj_func=problem_function,
    search_space=problem_bounds,
    debug_mode=True,
    num_iterations=1000,
)
```

For more examples, please, visit [Neuroptimiser Usage](https://neuroptimiser.github.io/usage.html)

## 📊 Benchmarking
Neuroptimiser has been validated over the [BBOB suite](https://github.com/numbbo/coco), showing:
* Competitive convergence versus Random Search
* Consistent results across function types and dimensions
* Linear runtime scaling with number of units and problem size

## 🔬 Citation
```bibtex
@misc{neuroptimiser2025,
  author={Cruz-Duarte, Jorge M. and Talbi, El-Ghazali},
  title        = {Neuroptimiser: A neuromorphic optimisation framework},
  year         = {2025},
  url          = {https://github.com/neuroptimiser/neuroptimiser},
  note         = {Version 1.0.X, accessed on 20XX-XX-XX}
}
```

## 🔗 Resources
* 📘 [Documentation](https://neuroptimiser.github.io)
* 📜 [Paper](https://doi.org/10.48550/arXiv.2507.08320)
* 🧠 [Intel Lava-NC](https://github.com/lava-nc/lava)
* 🧪 [COCO Platform](https://github.com/numbbo/coco)

## 🛠️ License
BSD-3-Clause License — [see LICENSE](LICENSE)

## 🧑‍💻 Authors
* [Jorge M. Cruz-Duarte](https://github.com/jcrvz) — University of Lille
* El-Ghazali Talbi — University of Lille