Impact of Neuron Models on Spiking Neural Network Performance: A Complexity-based Classification Approach.

This study addresses the important question of how neuron model choice and learning rules shape the classification performance of Spiking Neural Networks (SNNs) in bio-signal processing. By systematically contrasting Leaky Integrate-and-Fire, metaneurons, and probabilistic Levy-Baxter (LB) neurons across spike-timing dependent plasticity, tempotron, and reward-modulated learning, we identify model-rule combinations best suited for capturing the temporal richness of neural data. A novel contribution is the integration of a complexity-driven evaluation into the SNN pipeline. Using Lempel-Ziv Complexity (LZC), an entropy-related measure of spike-train regularity, we provide a consistent and interpretable benchmark of classification outcomes across architectures. To probe neural dynamics under controlled conditions, we employed synthetic datasets with varying temporal dependencies and stochasticity, including Markov and Poisson processes established models of neuronal spike-trains. Moreover, we validated the observed trends on real data by testing the same architectures on an MNIST dataset. Performance trends reveal strong dependence on the interaction between neuron model, learning rule, and network size. The LZC based evaluation highlights configurations resilient to weak or noisy signals. The LB-tempotron combination proved most effective for tasks with complex temporal patterns, leveraging adaptive neuronal dynamics and precise spike-timing exploitation. LIF-based architectures with Bio-inspired Active Learning delivered solid accuracy at lower computational cost, while hybrid models offered a versatile middle ground when paired with appropriate learning algorithms. This work delivers the first systematic mapping of neuron model learning rule synergies in SNNs and introduces complexity-based evaluation framework that sets a robust benchmark for biosignal classification. Beyond benchmarking, our results provide actionable guidelines for building next-generation SNNs capable of handling the variability and complexity of real neural data.