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


  • 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
  • Kay Chen Tan The Hong Kong Polytechnic University



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


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



How to Cite

Zhang, S., Yang, Q., Ma, C., Wu, J., Li, H., & Tan, K. C. (2024). TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling. Proceedings of the AAAI Conference on Artificial Intelligence, 38(15), 16838-16847.



AAAI Technical Track on Machine Learning VI