Enhancing time scalability of spiking neural networks with dynamic time constants.

In recent years, Spiking Neural Networks (SNNs) have received attention due to their high biological plausibility. They are expected to have superior temporal information processing capabilities because they describe the dynamics of brain neurons in a form suitable for neuromorphic computing. The time constant in the neuron model is important for enhancing the temporal representation of SNNs. Although algorithms that allow the time constant to be learned have often been proposed, they face challenges in generalizing performance to input patterns executed at speeds not seen during training, such as gestures performed at different speeds. This paper aims to develop a robust SNN capable of generalizing to diverse input speeds from training at a single reference speed. We introduce a mechanism that dynamically adapts the time constant of the SNN in response to the input speed. By applying this mechanism to SNNs, we showed that the membrane potential could be approximated by time-scaling according to the input speed. Additionally, we demonstrated experimentally that the relationship between the input and membrane potential holds in SNNs with a general network structure. Using this method, gesture classification and manipulator trajectory prediction were conducted. The experimental results indicate that the proposed method improves the generalization performance of the classification and prediction even for input patterns performed at speeds not seen during training, thereby enhancing the time scalability of SNNs.