Computational modelling of salamander retinal ganglion cells using machine learning approaches

NEUROCOMPUTING

卷:325

Article

Das, Gautham P.¹  Vance, Philip J.²  Kerr, Dermot²  Coleman, Sonya A.²  McGinnity, Thomas M.³  Liu, Jian K.⁴ 

[1] Amrita Univ, Dept Mech Engn, Amrita Sch Engn, Amrita Vishwa Vidyapeetham, Amritapuri, India

[2] Univ Ulster, Intelligent Syst Res Ctr, Magee Campus, Coleraine, Londonderry, North Ireland

[3] Nottingham Trent Univ, Coll Sci & Technol, Nottingham, England

[4] Graz Univ Technol, Inst Theoret Comp Sci, Graz, Austria

关键词: Artificial vision;    Biological vision;    Machine learning;    Retinal Ganglion cell;   

DOI  :  10.1016/j.neucom.2018.10.004

来源: Elsevier

【 摘 要 】

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. Crown Copyright (C) 2018 Published by Elsevier B.V. All rights reserved.

【 授权许可】

Free