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Bio-Inspired Approach to Modelling Retinal Ganglion Cells using System Identification Techniques

Vance, Philip, Das, Gautham, Kerr, Dermot, Coleman, Sonya, McGinnity, T.Martin, Gollisch, Tim and Liu, Jian (2018) Bio-Inspired Approach to Modelling Retinal Ganglion Cells using System Identification Techniques. IEEE Transactions on Neural Networks and Learning Systems, 29 (5). pp. 1796-1808. [Journal article]

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DOI: 10.1109/TNNLS.2017.2690139


The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power and performance. A key aspect to modelling the human visual system is the ability to accurately model the behaviour and computation within the retina. In particular, we focus on modelling the retinal ganglion cells as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within retinal ganglion cells can be derived by quantitatively fitting sets of physiological data using an input-output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input-output responses are modelled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this work, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behaviour, and are a viable alternative to traditional linear-nonlinear approaches.

Item Type:Journal article
Keywords:Retinal ganglion cells, computational modelling, biological vision, receptive field, artificial stimuli.
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:37390
Deposited By: Dr Dermot Kerr
Deposited On:07 Apr 2017 10:29
Last Modified:23 Apr 2018 11:17

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