TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues. To address this challenge, we propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF. The proposed model incorporates carefully designed somatic and dendritic compartments that are tailored to facilitate learning long-term temporal dependencies. Furthermore, the theoretical analysis is provided to validate the effectiveness of TC-LIF in propagating error gradients over an extended temporal duration. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, and high energy efficiency of the proposed TC-LIF model. Therefore, this work opens up a myriad of opportunities for solving challenging temporal processing tasks on emerging neuromorphic computing systems. Our code is publicly available at https://github.com/ZhangShimin1/TC-LIF.

Authors

• Shimin Zhang The Hong Kong Polytechnic University
• Qu Yang National University of Singapore
• Chenxiang Ma The Hong Kong Polytechnic University
• Jibin Wu The Hong Kong Polytechnic University
• Haizhou Li The Chinese University of Hong Kong, Shenzhen National University of Singapore Shenzhen Research Institute of Big Data
• Kay Chen Tan The Hong Kong Polytechnic University

Keywords

• CMS: Other Foundations of Cognitive Modeling & Systems
• ML: Bio-inspired Learning