Modelling fast forms of visual neural plasticity using a modified second-order motion energy model

Pavan, Andrea and Contillo, Adriano and Mather, George (2014) Modelling fast forms of visual neural plasticity using a modified second-order motion energy model. Journal of Computational Neuroscience, 37 (3). pp. 493-504. ISSN 0929-5313

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Modelling fast forms of visual neural plasticity using a modified second-order motion energy model

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14708 Extended Motion Energy Model - Accepted.pdf
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Abstract

The Adelson-Bergen motion energy sensor is well established as the leading model of low-level visual motion sensing in human vision. However, the standard model cannot predict adaptation effects in motion perception. A previous paper Pavan et al.(Journal of Vision 10:1-17, 2013) presented an extension to the model which uses a first-order RC gain-control circuit (leaky integrator) to implement adaptation effects which can span many seconds, and showed that the extended model's output is consistent with psychophysical data on the classic motion after-effect. Recent psychophysical research has reported adaptation over much shorter time periods, spanning just a few hundred milliseconds. The present paper further extends the sensor model to implement rapid adaptation, by adding a second-order RC circuit which causes the sensor to require a finite amount of time to react to a sudden change in stimulation. The output of the new sensor accounts accurately for psychophysical data on rapid forms of facilitation (rapid visual motion priming, rVMP) and suppression (rapid motion after-effect, rMAE). Changes in natural scene content occur over multiple time scales, and multi-stage leaky integrators of the kind proposed here offer a computational scheme for modelling adaptation over multiple time scales. © 2014 Springer Science+Business Media New York.

Keywords:Short-term neural plasticity, Rapid visual motion priming, Rapid motion after-effect, Second-order RC integrator, Motion energy, NotOAChecked
Subjects:G Mathematical and Computer Sciences > G750 Cognitive Modelling
C Biological Sciences > C850 Cognitive Psychology
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Social Science > School of Psychology
ID Code:14708
Deposited On:19 Aug 2014 15:56

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