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Computational Models of Neural Retina

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Encyclopedia of Computational Neuroscience

Synonyms

Biophysical models; Computational models of retinal function; Retinal network models

Definition

Computational models of the neural retina are extremely useful in the understanding of normal and abnormal retinal responses to light. They can also be for investigating retinal responses to artificial stimuli such as electrical stimulation by a vision prosthesis. Depending on the aims, complexities, and physiological assumptions on which the model is based, computational models of the retina may be classified into six broad types: single-compartment, morphologically realistic, block-compartment, discrete-neuronal network, continuum, and empirical models of retinal function.

Introduction

The retina is an elaborate architecture of neurons interconnected through gap junctions and synapses. At the outer retina, an array of rod and cone photoreceptors converts the incident light to neural responses. These signals then pass through approximately ten types of cone and one type of rod...

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Authors and Affiliations

  1. Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia

    Socrates Dokos & Tianruo Guo

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  1. Socrates Dokos
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  2. Tianruo Guo
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Correspondence to Socrates Dokos .

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    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, FL, USA

    Ranu Jung

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  1. Graduate School of Biomedical Engineering, UNSW Australia, Sydney, NSW, Australia

    Nigel H. Lovell

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Dokos, S., Guo, T. (2020). Computational Models of Neural Retina. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_652-2

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_652-2

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  1. Latest

    Computational Models of Neural Retina
    Published:
    17 March 2020

    DOI: https://doi.org/10.1007/978-1-4614-7320-6_652-2

  2. Original

    Computational Models of Neural Retina
    Published:
    31 March 2014

    DOI: https://doi.org/10.1007/978-1-4614-7320-6_652-1

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