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.

翻译：动态膜电位阈值作为生物神经元的基本特性之一，是一种维持神经元稳态（即神经元整体脉冲发放率恒定）的自发调节机制。因此，神经元放电率受动态脉冲阈值调控，这一现象已在生物学领域得到广泛研究。现有机器学习领域的研究未采用生物启发的脉冲阈值方案。本文旨在通过为脉冲神经网络（SNN）引入新型生物启发式动态能量-时间阈值（BDETT）方案来弥合这一差距。所提出的BDETT方案模拟了两种生物可观测现象：动态阈值与1）平均膜电位呈正相关，2）与先前去极化速率呈负相关。我们在常规条件及多种退化条件（包括含噪声观测、噪声权重及动态环境）下，验证了所提BDETT在机器人避障和连续控制任务中的有效性。研究发现，在所有测试条件下，BDETT显著优于现有静态及启发式阈值方法，并证实所提出的生物启发动态阈值方案能为复杂现实任务中的SNN提供稳态调节。