  The dynamic membrane potential threshold, as one of the essential properties
of a biological neuron, is a spontaneous regulation mechanism that maintains
neuronal homeostasis, i.e., the constant overall spiking firing rate of a
neuron. As such, the neuron firing rate is regulated by a dynamic spiking
threshold, which has been extensively studied in biology. Existing work in the
machine learning community does not employ bioinspired spiking threshold
schemes. This work aims at bridging this gap by introducing a novel bioinspired
dynamic energy-temporal threshold (BDETT) scheme for spiking neural networks
(SNNs). The proposed BDETT scheme mirrors two bioplausible observations: a
dynamic threshold has 1) a positive correlation with the average membrane
potential and 2) a negative correlation with the preceding rate of
depolarization. We validate the effectiveness of the proposed BDETT on robot
obstacle avoidance and continuous control tasks under both normal conditions
and various degraded conditions, including noisy observations, weights, and
dynamic environments. We find that the BDETT outperforms existing static and
heuristic threshold approaches by significant margins in all tested conditions,
and we confirm that the proposed bioinspired dynamic threshold scheme offers
homeostasis to SNNs in complex real-world tasks.
