Evolving spiking neural networks: the role of neuron models and encoding schemes in neuromorphic learning

This study investigates the impact of neuron models and encoding schemes on the performance of spiking neural networks trained using the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. By evaluating both classification and reinforcement learning tasks, we compare the performance of the Leaky Integrate-and-Fire (LIF) and Izhikevich neuron models across various input and output coding strategies. Our results demonstrate that the Izhikevich model consistently outperforms the simpler LIF model, except in one task where both showed comparable results. These findings emphasize that the choice of neuron model is as critical as encoding schemes in neuromorphic learning and highlight the importance of task-specific configuration. The study also showcases the potential of simulation frameworks for prototyping and optimizing neuromorphic systems.