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ESANN
2004

A biologically plausible neuromorphic system for object recognition and depth analysis

9 years 11 months ago
A biologically plausible neuromorphic system for object recognition and depth analysis
Abstract. We present a large-scale Neuromorphic model based on integrateand-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.
Zhijun Yang, Alan F. Murray
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where ESANN
Authors Zhijun Yang, Alan F. Murray
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