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Refining Receptive Field Estimates using Natural Images for Retinal Ganglion Cells

Vance, Philip, Das, Gautham, Kerr, Dermot, Coleman, SA and McGinnity, T.Martin (2016) Refining Receptive Field Estimates using Natural Images for Retinal Ganglion Cells. In: COGNITIVE 2016 : The Eighth International Conference on Advanced Cognitive Technologies and Applications, Rome, Italy. IARIA. 6 pp. [Conference contribution]

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URL: http://www.thinkmind.org/index.php?view=article&articleid=cognitive_2016_5_10_40073

Abstract

Determining the structure and size of a retinal ganglion cell’s receptive field is critically important when formulating a computational model to describe the relationship between stimulus and response. This is commonly achieved using a process of reverse correlation through stimulation of the retinal ganglion cell with artificial stimuli (for example bars or gratings) in a controlled environment. It has been argued however, that artificial stimuli are generally not complex enough to encapsulate the full complexity of a visual scene’s stimuli and thus any model formulated under these conditions can only be considered to emulate a subset of the biological model. In this paper, we present an investigation into the use of natural images to refine the size of the receptive fields, where their initial location and shape have been pre-determined through reverse correlation. We present findings that show the use of natural images to determine the receptive field size provides a significant improvement over the standard approach for determining the receptive field.

Item Type:Conference contribution (Paper)
Keywords:receptive field; retinal ganglion cell; retina; vision system; natural images.
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:36097
Deposited By: Dr Sonya Coleman
Deposited On:23 Feb 2017 16:38
Last Modified:23 Feb 2017 16:38

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