Analyzing Internal Activity and Robustness of SNNs Across Neuron Parameter Space
This work provides practical guidelines for deploying robust and efficient SNNs, particularly in neuromorphic computing, by addressing hyperparameter tuning for energy efficiency and performance.
The study identified a constrained region in the neuron hyperparameter space (membrane time constant and voltage threshold) where Spiking Neural Networks (SNNs) achieve optimal trade-offs between classification accuracy and spiking activity, with deviations leading to excessive energy use or network silence. It also found that SNNs operating outside this region show increased spike correlation and internal synchrony under adversarial noise.
Spiking Neural Networks (SNNs) offer energy-efficient and biologically plausible alternatives to traditional artificial neural networks, but their performance depends critically on the tuning of neuron model parameters. In this work, we identify and characterize an operational space - a constrained region in the neuron hyperparameter domain (specifically membrane time constant tau and voltage threshold vth) - within which the network exhibits meaningful activity and functional behavior. Operating inside this manifold yields optimal trade-offs between classification accuracy and spiking activity, while stepping outside leads to degeneration: either excessive energy use or complete network silence. Through systematic exploration across datasets and architectures, we visualize and quantify this manifold and identify efficient operating points. We further assess robustness to adversarial noise, showing that SNNs exhibit increased spike correlation and internal synchrony when operating outside their optimal region. These findings highlight the importance of principled hyperparameter tuning to ensure both task performance and energy efficiency. Our results offer practical guidelines for deploying robust and efficient SNNs, particularly in neuromorphic computing scenarios.