Tools & Resources

Explore frameworks, libraries, and hardware resources for neuromorphic computing and optimisation.

NEVO: Neuromorphic EVolutionary Optimisation

framework

NEVO uses a Nengo-simulated Basal Ganglia circuit to adaptively select optimisation operators at runtime, bridging neuromorphic computing with metaheuristic search.

GitHub Docs

Nengo Basal Ganglia Neuromorphic Optimisation Evolutionary Computation TD Learning Python

NeurOptimiser

framework

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

GitHub Docs PyPI

Lava Neuromorphic Optimization Evolutionary Computation Event-Driven Python

NeurOptimiser Dataset

dataset

Experiment codes and datasets for the NeurOptimiser framework, including BBOB benchmark results with linear and Izhikevich spiking neuron models.

GitHub Docs Zenodo

Dataset Neuromorphic Optimisation Spiking Neural Networks Differential Evolution Metaheuristics Izhikevich BBOB Benchmarking Lava

NeurOptimisation: The Spiking Way to Evolve

dataset

Experiment codes and datasets for the WCCI 2026 paper, featuring comprehensive experiments on BBOB test suite with linear and Izhikevich spiking neuron models.

GitHub Docs Zenodo

Dataset Neuromorphic Optimisation Spiking Neural Networks Izhikevich Differential Evolution Evolutionary Computation BBOB Lava

NEVO Dataset

dataset

Codes and experimental results for NEVO, a neuromorphic evolutionary optimiser with spike-driven Cortico-Basal-Thalamic coordination, evaluated on the BBOB benchmark suite.

GitHub Docs Zenodo

Dataset Neuromorphic Optimisation Evolutionary Computation Spiking Neural Networks Cortico-Basal-Thalamic Loop BBOB Nengo