Bioinspired Approach to Modeling Retinal Ganglion Cells Using System Identification Techniques

Vance, Philip J., Das, Gautham, Kerr, Dermot , Coleman, Sonya A., McGinnity, T. Martin, Gollisch, Tim and Liu, Jian K. (2018) Bioinspired Approach to Modeling Retinal Ganglion Cells Using System Identification Techniques. IEEE Transactions on Neural Networks and Learning Systems, 29 (5). pp. 1796-1808. ISSN 2162-2388

Full content URL: https://doi.org/10.1109/TNNLS.2017.2690139

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Bioinspired Approach to Modeling Retinal Ganglion Cells Using System Identification Techniques
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Abstract

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 modeling the human visual system is the ability to accurately model the behavior and computation within the retina. In particular, we focus on modeling the retinal ganglion cells (RGCs) 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 RGCs can be derived by quantitatively fitting the 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 modeled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this paper, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behavior, and are a viable alternative to traditional linear–nonlinear approaches.

Keywords:artifical stimuli, biological vision, compuational modelling, receptive field, retinal ganglion cells
Subjects:G Mathematical and Computer Sciences > G750 Cognitive Modelling
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:40819
Deposited On:30 Sep 2020 11:38

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