Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/108926
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Type: Conference paper
Title: Salience invariance with divisive normalization in higher-order insect neurons
Author: Evans, B.
O'Carroll, D.
Wiederman, S.
Citation: Proceedings of the 2016 6th European Workshop on Visual Information Processing, 2016 / Beghdadi, A., Bourennane, S., Bouzerdoum, A., Pedersen, M., Oudre, L., Jiang, R. (ed./s), pp.1-6
Publisher: IEEE
Issue Date: 2016
ISBN: 9781509027811
Conference Name: 6th European Workshop on Visual Information Processing (EUVIP 2016) (25 Oct 2016 - 27 Oct 2016 : Marseille, France)
Editor: Beghdadi, A.
Bourennane, S.
Bouzerdoum, A.
Pedersen, M.
Oudre, L.
Jiang, R.
Statement of
Responsibility: 
Bernard J. E. Evans, David C. O, Carroll, and Steven D. Wiederman
Abstract: We present a biologically inspired model for estimating the position of a moving target that is invariant to the target's contrast. Our model produces a monotonic relationship between position and output activity using a divisive normalization between the 'receptive fields' of two overlapping, wide-field, small-target motion detector (STMD) neurons. These visual neurons found in flying insects, likely underlie the impressive ability to pursue prey within cluttered environments. Individual STMD responses confound the properties of target contrast, size, velocity and position. Inspired by results from STMD recordings we developed a model using a division operation to overcome the inherent positional ambiguities of integrative neurons. We used genetic algorithms to determine the plausibility of such an operation arising and existing over multiple generations. This method allows the lost information to be recovered without needing additional neuronal pathways.
Keywords: Biological neural networks, biological system modeling, cellular biophysics, genetic algorithms, physiology
Rights: © 2016 IEEE
DOI: 10.1109/EUVIP.2016.7764588
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