Framework v1.0.2 MIT

NeurOptimiser

NeurOptimiser is a neuromorphic optimisation framework in which metaheuristic search emerges from asynchronous spiking dynamics.

GitHub Documentation PyPI

Installation

pip install neuroptimiser

Lava Neuromorphic Optimization Evolutionary Computation Event-Driven Python

NeurOptimiser is a neuromorphic optimisation framework in which metaheuristic search emerges from asynchronous spiking dynamics.

Overview

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.

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.

Quick Start

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,
)

Benchmarking

Neuroptimiser has been validated over the BBOB suite, showing:

Competitive convergence versus Random Search
Consistent results across function types and dimensions
Linear runtime scaling with number of units and problem size