Computational Approach to Identifying Contrast-Driven Retinal Ganglion Cells

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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 publicationProceedings of the European Neural Network Society
Pages635-646
Number of pages12
Volume12893
DOIs
Publication statusPublished (in print/issue) - 7 Sept 2021
EventThe 30th International Conference on Artificial Neural Networks: ICANN 2021 -
Duration: 14 Sept 202117 Sept 2021

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThe 30th International Conference on Artificial Neural Networks
Period14/09/2117/09/21

Keywords

  • retinal modelling
  • encoding natural images
  • identifying cell behaviour
  • visual modelling

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