Enterprise AI Analysis

Executive Summary: Optimizing SNN Performance

This study rigorously investigates how neuron model selection and learning rules affect the classification performance of Spiking Neural Networks (SNNs), particularly in bio-signal processing. By systematically comparing Leaky Integrate-and-Fire (LIF), metaneurons, and probabilistic Levy-Baxter (LB) neurons with spike-timing dependent plasticity (STDP), tempotron, and reward-modulated learning, we identify optimal model-rule combinations. A novel complexity-driven evaluation using Lempel-Ziv Complexity (LZC) is integrated into the SNN pipeline, providing an interpretable benchmark of classification outcomes. Synthetic datasets with varying temporal dependencies (Markov, Poisson) and real MNIST data were used to validate the findings. The results highlight strong dependencies on neuron model, learning rule, and network size, with LZC evaluation identifying robust configurations. The LB-tempotron combination proved most effective for complex temporal pattern tasks, leveraging adaptive dynamics and precise spike-timing exploitation. LIF-based architectures with Bio-inspired Active Learning offered solid accuracy at lower computational cost, while hybrid models provided a versatile middle ground. This work provides systematic guidelines for building next-generation SNNs for real neural data.