An Optimization Framework for Spiking Neuron Models: Parameter Estimation, Network Validation, and Pattern Separation
Accurate and scalable fitting of spiking neuron models to experimental electrophysiological data remains a significant challenge due to its large parametric space. The current paper introduces a hybrid optimization method to address the challenge of parameter fitting on spiking neuron models mapping them into experimental constraints. A sequential hybrid approach combining Differential Evolution (DE) and Nelder–Mead (NM) was used to fit point neuron model parameters to experimental benchmarks of cerebellar neurons. Cerebellum spiking dynamics were optimized with the hybrid algorithm and was compared to Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) under both parallelized and non-parallelized simulations. The robustness was tested under various constraint conditions, and the pattern separation analysis was assessed on public datasets. Statistical comparisons indicated that the hybrid DE-NM approach outperformed GA and PSO across various modelled neuron types, with faster convergence and lower optimization errors. Mean absolute errors were observed to be below 5 Hz and success rates above 75% across neuron types. The optimized models were used to reconstruct the cerebellar input layer network, reproducing theta-band resonance, sparse coding, and pattern separation. A GUI-based tool, NeuronOpt, was implemented on the workflow improving the accessibility and repeatability of the approach and allowing the method reusable beyond cerebellar neurons. The tool was validated with cerebellum neural and input layer circuit dynamics. The tool and the methodology allow computational reliability of the hybrid optimization framework to be used for optimization across multiple applications.