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：作为生物神经元体的基本特性之一,动态膜潜在临界值是一种自发调节机制,它保持神经神经元的软态状态,即神经神经神经元的不断全面发热率。因此,神经发热率受生物领域广泛研究的动态喷发临界值的调节。机器学习界的现有工作并不采用生物刺激的喷发临界值计划。这项工作的目的是通过引入一种全新的生物激发的动态能量-时空阈值(BDETT)计划来弥补这一差距,为神经神经网络(SNNS)推出一种全新的生物激发的动态调节机制。拟议的BDETT方案反映了两种生物可复制性观测结果:1)动态阈值与平均膜潜在值呈正相关关系,2)与先前的脱热率呈负相关关系。我们验证了拟议的BDETT在正常条件下和各种退化条件下避免机器人障碍和持续控制任务的有效性,包括噪音观测、重量和动态环境。我们发现,BDETTT方案超越了现有的静态和超常阈值临界值,所有经过测试的动态阈值,我们确认拟议的生物-NNFM计划提供了生物效应。