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http://hdl.handle.net/10397/107479
Title: | A hybrid neural coding approach for pattern recognition with spiking neural networks | Authors: | Chen, X Yang, Q Wu, J Li, H Tan, KC |
Issue Date: | May-2024 | Source: | IEEE transactions on pattern analysis and machine intelligence, May 2024, v. 46, no. 5, p. 3064-3078 | Abstract: | Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. Given that each neural coding scheme possesses its own merits and drawbacks, these SNNs encounter challenges in achieving optimal performance such as accuracy, response time, efficiency, and robustness, all of which are crucial for practical applications. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes. As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience. Additionally, it incorporates a flexible neural coding assignment strategy to accommodate task-specific requirements, along with novel layer-wise learning methods to effectively implement hybrid coding SNNs. We demonstrate the superiority of the proposed framework on image classification and sound localization tasks. Specifically, the proposed hybrid coding SNNs achieve comparable accuracy to state-of-the-art SNNs, while exhibiting significantly reduced inference latency and energy consumption, as well as high noise robustness. This study yields valuable insights into hybrid neural coding designs, paving the way for developing high-performance neuromorphic systems. | Keywords: | Hybrid neural coding and learning framework Layer-wise learning Neural coding Neuromorphic computing Spiking neural network |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on pattern analysis and machine intelligence | ISSN: | 0162-8828 | EISSN: | 1939-3539 | DOI: | 10.1109/TPAMI.2023.3339211 | Rights: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication X. Chen, Q. Yang, J. Wu, H. Li and K. C. Tan, "A Hybrid Neural Coding Approach for Pattern Recognition With Spiking Neural Networks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 3064-3078, May 2024 is available at https://doi.org/10.1109/TPAMI.2023.3339211. |
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