Computational Approach to Identifying Contrast-Driven Retinal Ganglion Cells

Richard Gault, Philip Vance, T.M McGinnity, Sonya Coleman, Dermot Kerr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The retina acts as the primary stage for the encoding
of visual stimuli in the central nervous system. It is comprised of numerous functionally distinct cells tuned to particular types of visual stimuli. This work presents an analytical approach to identifying contrast driven retinal cells. Machine learning approaches as well as traditional regression models are used to represent the input-output behaviour of retinal ganglion cells. The findings of this work demonstrate that it is possible to separate the cells based on how they respond to changes in mean contrast upon presentation of single images. The separation
allows us to identify retinal ganglion cells that are likely to have good model performance in a computationally inexpensive way.
Original languageEnglish
Title of host publicationInternational Conference on Artificial Neural Networks
PublisherSpringer Lecture Notes in Computer Science (LNCS)
Publication statusAccepted - 15 Jun 2021
Event30th International Conference on Artificial Neural Networks -
Duration: 14 Sep 202117 Sep 2021
Conference number: 30
https://e-nns.org/icann2021/

Conference

Conference30th International Conference on Artificial Neural Networks
Abbreviated titleICANN
Period14/09/202117/09/2021
Internet address

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