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Title: | TC-LIF : a two-compartment spiking neuron model for long-term sequential modelling | Authors: | Zhang, S Yang, Q Ma, C Wu, J Li, H Tan, KC |
Issue Date: | 2024 | Source: | In M Wooldridge, J Dy, & S Natarajan (Eds.), Proceedings of the 38th AAAI Conference on Artificial Intelligence, p. 16838-16847. Washington, DC: Association for the Advancement of Artificial Intelligence, 2024 | Abstract: | The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by un-related 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 so-matic and dendritic compartments that are tailored to facilitate learning long-term temporal dependencies. Furthermore, a 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. | Publisher: | Association for the Advancement of Artificial Intelligence | ISBN: | 1-57735-887-2 978-1-57735-887-9 |
DOI: | 10.1609/aaai.v38i15.29625 | Description: | Thirty-Eighth AAAI Conference on Artificial Intelligence, February 20–27, 2024, Vancouver, Canada | Rights: | Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The following publication Zhang, S., Yang, Q., Ma, C., Wu, J., Li, H., & Tan, K. C. (2024, March). Tc-lif: A two-compartment spiking neuron model for long-term sequential modelling. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 15, pp. 16838-16847) is available at https://ojs.aaai.org/index.php/AAAI/article/view/29625. |
Appears in Collections: | Conference Paper |
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