FEEDFORWARD COMPUTATIONAL MODEL FOR PATTERN RECOGNITION WITH SPIKING NEURONS
Malu Zhang, Hong Qu, Jianping Li, Ammar Belatreche, Xiurui Xie, and Zhi Zeng
Keywords
Spiking neurons, computational model, spiking neural networks, pattern recognition
Abstract
Humans and primates are remarkably good at pattern recognition
and outperform the best machine vision systems with respect
to almost any measure. Building a computational model that
emulates the architecture and information processing in biological
neural systems has always been an attractive target. To build a
computational model that closely follows the information processing
and architecture of the visual cortex, in this paper, we have improved
the latency-phase encoding to express the external stimuli in a
more abstract manner. Moreover, inspired by recent findings in
the biological neural system, including architecture, encoding, and
learning theories, we have proposed a feedforward computational
model of spiking neurons that emulates object recognition of the
visual cortex for pattern recognition. Simulation results showed that
the proposed computational model can perform pattern recognition
task well. In addition, the success of this computational model
suggests a plausible proof for feedforward architecture of pattern
recognition in the visual cortex.
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