neuroptimiser
[](https://pypi.org/project/neuroptimiser/)
[](https://pypi.org/project/neuroptimiser/)
[](https://pypi.org/project/neuroptimiser/)
[](https://arxiv.org/abs/2507.08320)
[](https://doi.org/10.5281/zenodo.15592900)
[](https://doi.org/10.5281/zenodo.15858610)
# 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
```bash
pip install -e .
```
Ensure you have Python ≥ 3.10 and the Lava-NC environment configured.
You can also clone the repository and install it. Check the Makefile for additional options.
## 🚀 Example Usage
```python
from neuroptimiser import NeuroOptimiser
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
MIT License — [see LICENSE](LICENSE)
## 🧑💻 Authors
* [Jorge M. Cruz-Duarte](https://github.com/jcrvz) — University of Lille
* El-Ghazali Talbi — University of Lille
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