HoSNNs: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds

Abstract

While spiking neural networks (SNNs) offer a promising neurally-inspired model of computation, they are vulnerable to adversarial attacks. We present the first study that draws inspiration from neural homeostasis to design a threshold-adapting leaky integrate-and-fire (TA-LIF) neuron model and utilize TA-LIF neurons to construct the adversarially robust homeostatic SNNs (HoSNNs) for improved robustness. The TA-LIF model incorporates a self-stabilizing dynamic thresholding mechanism, offering a local feedback control solution to the minimization of each neuron's membrane potential error caused by adversarial disturbance. Theoretical analysis demonstrates favorable dynamic properties of TA-LIF neurons in terms of the bounded-input bounded-output stability and suppressed time growth of membrane potential error, underscoring their superior robustness compared with the standard LIF neurons. When trained with weak FGSM attacks (\(\epsilon = 2/255\)), our HoSNNs significantly outperform conventionally trained LIF-based SNNs across multiple datasets. Furthermore, under significantly stronger PGD7 attacks (\(\epsilon = 8/255\)), HoSNN achieves notable improvements in accuracy, increasing from 30.90% to 74.91% on FashionMNIST, 0.44% to 36.82% on SVHN, 0.54% to 43.33% on CIFAR10, and 0.04% to 16.66% on CIFAR100.