Bioinspired approach to modeling retinal ganglion cells using system identification techniques

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Vance, P.J., Das, G.P., Kerr, D., Coleman, S.A., McGinnity, T.M. ORCID: 0000-0002-9897-4748, Gollisch, T. and Liu, J.K., 2017. Bioinspired approach to modeling retinal ganglion cells using system identification techniques. IEEE Transactions on Neural Networks and Learning Systems, PP (99). ISSN 2162-237X

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Official URL: http://doi.org/10.1109/tnnls.2017.2690139

Abstract

The processing capabilities of the biological vision system are still vastly superior to artificial vision, which has been an active area of research for over half a century. Current artificial vision techniques, motivated by this robust performance, integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power and performance. With respect to modelling the retina, this is due to an insufficient understanding of the complex interactions between the cells and their organisation within the system. The core components within this system are 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 these visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where the input is a known and its output is recorded. Techniques capable of mapping this input-output response involve 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 ganglion cell
behaviour, and are a viable alternative to traditional linear-nonlinear approaches.

Item Type:

Journal article

Alternative Title:

Bio-inspired approach to modeling retinal ganglion cells using system identification techniques

Publication Title:

IEEE Transactions on Neural Networks and Learning Systems

Creators:

Vance, P.J., Das, G.P., Kerr, D., Coleman, S.A., McGinnity, T.M., Gollisch, T. and Liu, J.K.

Publisher:

Institute of Electrical and Electronics Engineers (IEEE)

Date:

12 April 2017