  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 (attack budget = 2/255) and tested with much stronger
PGD attacks (attack budget = 8/255), our HoSNNs significantly improve model
accuracy on several datasets: from 30.54% to 74.91% on FashionMNIST, from 0.44%
to 35.06% on SVHN, from 0.56% to 42.63% on CIFAR10, from 0.04% to 16.66% on
CIFAR100, over the conventional LIF-based SNNs.
