Feedforward computational model for pattern recognition with spiking neurons

Zhang, Malu, Qu, Hong, Li, Jianping, Belatreche, Ammar, Xie, Xiurui and Zeng, Zhi (2018) Feedforward computational model for pattern recognition with spiking neurons. International Journal of Robotics and Automation, 33. pp. 206-5044. ISSN 1925-7090

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Final Version Feedforward computational model for pattern recognition with spiking neurons_accepted version.pdf - Accepted Version

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Official URL: http://dx.doi.org/10.2316/Journal.206.2018.1.206-5...

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.

Item Type: Article
Subjects: G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 24 May 2018 10:05
Last Modified: 11 Dec 2018 15:31
URI: http://nrl.northumbria.ac.uk/id/eprint/34354

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