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Brain-Like System for Audiovisual Person Authentication Based on Time-to-First Spike Coding
Abstract
The question of the neural code, or how neurons code information about stimuli, is not definitively answered. In addition to experimental research, computational modeling can help in getting closer to solving this problem. In this chapter, spiking neural network architectures for visual, auditory and integrated audiovisual pattern recognition and classification are described. The authors’ spiking neural network uses time to first spike as a code for saliency of input features. The system is trained and evaluated on the person authentication task. The chapter concludes that the time-to-first-spike coding scheme may not be suitable for this difficult task, nor for auditory processing. Other coding schemes and extensions of this spiking neural network are discussed as the topics of the future research.
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