ArticleFrontiers in neuroscience2026
Evolving spiking neural networks: the role of neuron models and encoding schemes in neuromorphic learning.
Article in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
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.
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