Abstract
Sensory signals are encoded and processed by neurons in the brain in a form of action potentials, also called spikes that carry clue information across both spatial and temporal dimensions. Learning of such a clue information could be challenging, especially considering the case of long-delayed reward. This temporal credit assignment problem has been solved by a new concept of aggregate-label learning that motivates the development of a family of multi-spike learning algorithms whose remarkable learning performance has been demonstrated. However, most of the current spike-based learning methods are developed without consideration of input temporal fluctuations that constitute a common source of variability in sensory signals such as speech. Therefore, robust spike-based learning under fluctuations of both compression and dilation remains intriguing for exploration. In this paper, we first show the time-warp invariant characteristic of a conductance-based neuron model, based on which we then develop a new multi-spike learning algorithm for time-warp-invariant processing. Experimental results for speech recognition highlight the outstanding robustness of our algorithm against temporal distortions as compared with other relevant spike-based methods. Therefore, our study successfully confirms the effectiveness of multi-spike learning for time-warp robustness, extending a new scope for spike-based processing and learning.
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This work was supported by the National Natural Science Foundation of China under Grant 62176179.
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Zhou, X., Liu, Y., Sun, W., Yu, Q. (2024). Time-Warp-Invariant Processing with Multi-spike Learning. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1962. Springer, Singapore. https://doi.org/10.1007/978-981-99-8132-8_2
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DOI: https://doi.org/10.1007/978-981-99-8132-8_2
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