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

Vance, Philip J., Das, Gautham, Coleman, Sonya A. , Kerr, Dermot, Kerr, Emmett P. and McGinnity, Thomas M. (2018) Investigation into Sub-Receptive Fields of Retinal Ganglion Cells with Natural Images. In: 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.

Full content URL: https://doi.org/10.1109/IJCNN.2018.8489324

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

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.

Keywords:retinal ganglion cells, receptive field, computational modelling, visual neuroscience
Subjects:G Mathematical and Computer Sciences > G750 Cognitive Modelling
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:40822
Deposited On:30 Sep 2020 11:40

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