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Investigation into Sub-Receptive Fields of Retinal Ganglion Cells with Natural Images

Vance, Philip J., Das, Gautham, Coleman, Sonya, Kerr, Dermot, Kerr, Emmett and McGinnity, Thomas Martin (2018) Investigation into Sub-Receptive Fields of Retinal Ganglion Cells with Natural Images. In: IEEE World Congress on Computational Intelligence (IEEE WCCI), Windsor Convention Centre, Rio de Janeiro, Brazil.. IEEE. 7 pp. [Conference contribution]

[img] Text - Accepted Version


Determining the receptive field of a retinal ganglion cell is critically important when formulating a computational model that maps the relationship between the stimulus and response. This process is traditionally undertaken using reverse correlation to estimate the receptive field. By stimulating the retina with artificial stimuli, such as alternating checkerboards, bars or gratings and recording the neural response it is possible to estimate the cell’s receptive field by analysing the stimuli that produced the response. Artificial stimuli such as white noise is known to not stimulate the full range of the cell’s responses. By using natural image stimuli, it is possible to estimate the receptive field and obtain a resulting model that more accurately mimics the cells’ responses to natural stimuli. This paper extends on previous work to seek further improvements in estimating a ganglion cell’s receptive field by considering that the receptive field can be divided into subunits. It is thought that these subunits may relate to receptive fields which are associated with bipolar retinal cells. The findings of this preliminary study show that by using subunits to define the receptive field we achieve a significant improvement over existing approaches when deriving computational models of the cell’s response.

Item Type:Conference contribution (Paper)
Keywords:retinal ganglion cell, receptive field, computational modelling, visual neuroscience.
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:39977
Deposited By: Dr Philip Vance
Deposited On:19 Apr 2018 15:15
Last Modified:19 Apr 2018 15:15

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