Analyzing Internal Activity and Robustness of SNNs Across Neuron Parameter Space

--Spiking Neural Networks (SNNs) offer energy-efficient and biologically plausible alternatives to traditional artificial neural networks, but their performance critically depends on the tuning of neuron model parameters. Operating inside this manifold yields optimal trade-offs between classification accuracy and spiking activity, while stepping outside leads to degeneration: either excessive energy consumption or complete network silence. Through systematic exploration across multiple datasets and architectures, we visualize and quantify this manifold and identify efficient operating points. We further complement this analysis with experiments on robustness to adversarial noise, showing that SNNs exhibit heightened spike correlations and internal synchrony when pushed outside their operational manifold. These findings underscore the importance of principled hyperparameter tuning, not only to achieve high task performance, but also to maintain the network's stability and energy efficiency. Our results provide practical guidelines for deploying robust and efficient SNNs, especially in neuromorphic computing scenarios. Artificial Intelligence (AI) has experienced rapid advancement, largely driven by deep Artificial Neural Networks (ANNs), which have demonstrated superior performance across a wide range of applications, including vision, language processing, and robotics. Despite this success, ANNs remain difficult to interpret due to their highly overparameterized nature, and their training heavily relies on empirical hyperparameter tuning.