Biologically-inspired means for rank-order encoding images: a quantitative analysis

Sen Bhattacharya, Basabdatta and Furber, Steve (2010) Biologically-inspired means for rank-order encoding images: a quantitative analysis. IEEE Transactions on Neural Networks, 21 (7). pp. 1087-1099. ISSN 1045-9227

Full content URL: http://dx.doi.org/10.1109/TNN.2010.2048339

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Item Type:Article
Item Status:Live Archive

Abstract

In this paper, we present biologically inspired means to enhance perceptually important information retrieval from rank-order encoded images. Validating a retinal model proposed by VanRullen and Thorpe, we observe that on average only up to 70% of the available information can be retrieved from rank-order encoded images. We propose a biologically inspired treatment to reduce losses due to a high correlation of adjacent basis vectors and introduce a filter-overlap correction algorithm (FoCal) based on the lateral inhibition technique used by sensory neurons to deal with data redundancy. We observe a more than 10% increase in perceptually important information recovery. Subsequently, we present a model of the primate retinal ganglion cell layout corresponding to the foveal-pit. We observe that information recovery using the foveal-pit model is possible only if FoCal is used in tandem. Furthermore, information recovery is similar for both the foveal-pit model and VanRullen and Thorpe's retinal model when used with FoCal. This is in spite of the fact that the foveal-pit model has four ganglion cell layers as in biology while VanRullen and Thorpe's retinal model has a 16-layer structure.

Additional Information:sfurber@manchester.ac.uk
Keywords:Ganglion cell, lateral inhibition, perceptually important information, rank-order codes, retinal model
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Engineering
ID Code:8830
Deposited On:09 Apr 2013 16:56

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