Time-Warp-Invariant Processing with Multi-spike Learning

Xiaohan Zhou, Yuzhe Liu, Wei Sun, Qiang Yu

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.