Computational modelling of salamander retinal ganglion cells using machine learning approaches

Das, G.P., Vance, P.J., Kerr, D., Coleman, S.A., McGinnity, T.M. ORCID: 0000-0002-9897-4748 and Liu, J.K., 2019. Computational modelling of salamander retinal ganglion cells using machine learning approaches. Neurocomputing, 325, pp. 101-112. ISSN 0925-2312

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Abstract

Artificial vision using computational models that can mimic biological vision is an area of ongoing research. One of the main themes within this research is the study of the retina and in particular, retinal ganglion cells which are responsible for encoding the visual stimuli. A common approach to modelling the internal processes of retinal ganglion cells is the use of a linear - non-linear cascade model, which models the cell's response using a linear filter followed by a static non-linearity. However, the resulting model is generally restrictive as it is often a poor estimator of the neuron's response. In this paper we present an alternative to the linear - non-linear model by modelling retinal ganglion cells using a number of machine learning techniques which have a proven track record for learning complex non-linearities in many different domains. A comparison of the model predicted spike rate shows that the machine learning models perform better than the standard linear - non-linear approach in the case of temporal white noise stimuli.

Item Type: Journal article
Publication Title: Neurocomputing
Creators: Das, G.P., Vance, P.J., Kerr, D., Coleman, S.A., McGinnity, T.M. and Liu, J.K.
Publisher: Elsevier B.V.
Date: 24 January 2019
Volume: 325
ISSN: 0925-2312
Identifiers:
NumberType
10.1016/j.neucom.2018.10.004DOI
S0925231218311780Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Depositing User: Jill Tomkinson
Date Added: 08 Aug 2018 15:28
Last Modified: 06 Oct 2019 03:00
URI: http://irep.ntu.ac.uk/id/eprint/34279

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