A biologically plausible neuromorphic system for object recognition and depth analysis

Yang, Zhijun ORCID: https://orcid.org/0000-0003-2615-4297 and Murray, Alan (2004) A biologically plausible neuromorphic system for object recognition and depth analysis. In: ESANN'2004 proceedings - 12th European Symposium on Artificial Neural Networks. d-side publi, pp. 157-162. ISBN 2930307048

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Abstract

We present a large-scale neuromorphic model based on integrate-and-fire (IF) neurons that analyses objects and their depth within a moving visual scene. A feature-based algorithm builds a luminosity receptor field as an artificial retina, in which the IF neurons act both as photoreceptors and processing units. We show that the IF neurons can trace an object's path and depth using an adaptive time-window and Temporally Asymmetric Hebbian (TAH) training.

Item Type: Book Section
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 9926
Useful Links:
Depositing User: Zhijun Yang
Date Deposited: 20 Feb 2013 12:49
Last Modified: 01 Aug 2019 07:50
URI: https://eprints.mdx.ac.uk/id/eprint/9926

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