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

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.



Titolo: Modelling fast forms of visual neural plasticity using a modified second-order motion energy model
Autori: 
CONTILLO, Adriano
mostra contributor esterni 
Data di pubblicazione: 2014
Rivista: 
JOURNAL OF COMPUTATIONAL NEUROSCIENCE  
Handle: http://hdl.handle.net/11392/2369360
Appare nelle tipologie:03.1 Articolo su rivista

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