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. [Book Section]

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.

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