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A neuronally based model of contrast gain adaptation in fly motion vision

Published online by Cambridge University Press:  22 August 2011

ZULEY RIVERA-ALVIDREZ
Affiliation:
Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona
ICHI LIN
Affiliation:
Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona
CHARLES M. HIGGINS*
Affiliation:
Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona Department of Neuroscience, University of Arizona, Tucson, Arizona
*
Address correspondence and reprint requests to: Charles M. Higgins, Department of Neuroscience, University of Arizona, 1040 E 4th St, Tucson, AZ 85721. E-mail: higgins@neurobio.arizona.edu

Abstract

Motion-sensitive neurons in the visual systems of many species, including humans, exhibit a depression of motion responses immediately after being exposed to rapidly moving images. This motion adaptation has been extensively studied in flies, but a neuronal mechanism that explains the most prominent component of adaptation, which occurs regardless of the direction of motion of the visual stimulus, has yet to be proposed. We identify a neuronal mechanism, namely frequency-dependent synaptic depression, which explains a number of the features of adaptation in mammalian motion-sensitive neurons and use it to model fly motion adaptation. While synaptic depression has been studied mainly in spiking cells, we use the same principles to develop a simple model for depression in a graded synapse. By incorporating this synaptic model into a neuronally based model for elementary motion detection, along with the implementation of a center-surround spatial band-pass filtering stage that mimics the interactions among a subset of visual neurons, we show that we can predict with remarkable success most of the qualitative features of adaptation observed in electrophysiological experiments. Our results support the idea that diverse species share common computational principles for processing visual motion and suggest that such principles could be neuronally implemented in very similar ways.

Type
Research Articles
Copyright
Copyright © Cambridge University Press 2011

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