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Critical Reviews™ in Biomedical Engineering
SJR: 0.207 SNIP: 0.376 CiteScore™: 0.79

ISSN Imprimer: 0278-940X
ISSN En ligne: 1943-619X

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Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.2014011732
pages 419-436

Understanding the Retina: A Review of Computational Models of the Retina from the Single Cell to the Network Level

Tianruo Guo
Graduate School of Biomedical Engineering, UNSW Australia, Sydney, NSW 2052, Australia
David Tsai
Graduate School of Biomedical Engineering, UNSW Australia, Sydney, NSW 2052, Australia; Howard Hughes Medical Institute, Biological Sciences, Bioelectronic Systems Lab, Electrical Engineering, Columbia University, New York, NY
Siwei Bai
Graduate School of Biomedical Engineering, University of New South Wales, NSW 2052, Australia
John W. Morley
School of Medicine, University of Western Sydney, Penrith, NSW, Australia
Gregg J. Suaning
Graduate School of Biomedical Engineering, UNSW Australia, Sydney, NSW 2052, Australia
Nigel H. Lovell
Graduate School of Biomedical Engineering, UNSW Australia, Sydney, NSW 2052, Australia
Socrates Dokos
Graduate School of Biomedical Engineering, UNSW Australia, Sydney, NSW 2052, Australia; Department of Biomedical Engineering, Faculty of Engineering, Kuala Lumpur 50603, Malaysia

RÉSUMÉ

The vertebrate retina is a clearly organized signal-processing system. It contains more than 60 different types of neurons, arranged in three distinct neural layers. Each cell type is believed to serve unique role(s) in encoding visual information. While we now have a relatively good understanding of the constituent cell types in the retina and some general ideas of their connectivity, with few exceptions, how the retinal circuitry performs computation remains poorly understood. Computational modeling has been commonly used to study the retina from the single cell to the network level. In this article, we begin by reviewing retinal modeling strategies and existing models. We then discuss in detail the significance and limitations of these models, and finally, we provide suggestions for the future development of retinal neural modeling.


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