Modelling Retinal Ganglion Cells Stimulated with Static Natural Images

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A standard approach to model retinal ganglion cells uses reverse correlation to construct a linear-nonlinear model using a cascade of a linear filter and a static nonlinearity. A major constraint with this technique is the need to use a radially symmetric stimulus, such as Gaussian white noise. Natural visual stimuli are required to generate a more realistic ganglion-cell model. However, natural visual stimuli significantly differ from white noise stimuli and are not radially symmetric. Therefore a more sophisticated modelling approach than the linear-nonlinear method is required for modelling ganglion cells stimulated with natural images. Machine learning algorithms have proved very capable in modelling complex non-linear systems in other scientific domains. In this paper, we report on the development of computational models, using different machine learning regression algorithms, that model retinal ganglion cells stimulated with natural images in order to predict the number of spikes elicited. Neuronal recordings obtained from electro-physiological experiments in which isolated salamander retinas are stimulated with static natural images are used to develop these models. In order to compare the performance of the machine learning models, a linear-nonlinear model was also developed from separate experiments using Gaussian white noise stimuli. A comparison of the spike prediction using the models developed shows that the machine learning models perform better than the linear-nonlinear approach.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages6
Publication statusPublished - 24 Mar 2016
EventCOGNITIVE 2016 : The Eighth International Conference on Advanced Cognitive Technologies and Applications - Rome, Italy
Duration: 24 Mar 2016 → …

Conference

ConferenceCOGNITIVE 2016 : The Eighth International Conference on Advanced Cognitive Technologies and Applications
Period24/03/16 → …

Fingerprint

Learning systems
White noise
Learning algorithms
Nonlinear systems
Experiments

Keywords

  • Retinal ganglion cells
  • Natural image stimulus
  • Linear-nonlinear models
  • Machine learning models

Cite this

@inproceedings{7f07754b7ca84f8baa348d6447db25c2,
title = "Modelling Retinal Ganglion Cells Stimulated with Static Natural Images",
abstract = "A standard approach to model retinal ganglion cells uses reverse correlation to construct a linear-nonlinear model using a cascade of a linear filter and a static nonlinearity. A major constraint with this technique is the need to use a radially symmetric stimulus, such as Gaussian white noise. Natural visual stimuli are required to generate a more realistic ganglion-cell model. However, natural visual stimuli significantly differ from white noise stimuli and are not radially symmetric. Therefore a more sophisticated modelling approach than the linear-nonlinear method is required for modelling ganglion cells stimulated with natural images. Machine learning algorithms have proved very capable in modelling complex non-linear systems in other scientific domains. In this paper, we report on the development of computational models, using different machine learning regression algorithms, that model retinal ganglion cells stimulated with natural images in order to predict the number of spikes elicited. Neuronal recordings obtained from electro-physiological experiments in which isolated salamander retinas are stimulated with static natural images are used to develop these models. In order to compare the performance of the machine learning models, a linear-nonlinear model was also developed from separate experiments using Gaussian white noise stimuli. A comparison of the spike prediction using the models developed shows that the machine learning models perform better than the linear-nonlinear approach.",
keywords = "Retinal ganglion cells, Natural image stimulus, Linear-nonlinear models, Machine learning models",
author = "Gautham Das and Philip Vance and Dermot Kerr and SA Coleman and T.Martin McGinnity",
year = "2016",
month = "3",
day = "24",
language = "English",
isbn = "978-1-61208-462-6",
booktitle = "Unknown Host Publication",

}

Das, G, Vance, P, Kerr, D, Coleman, SA & McGinnity, TM 2016, Modelling Retinal Ganglion Cells Stimulated with Static Natural Images. in Unknown Host Publication. COGNITIVE 2016 : The Eighth International Conference on Advanced Cognitive Technologies and Applications, 24/03/16.

Modelling Retinal Ganglion Cells Stimulated with Static Natural Images. / Das, Gautham; Vance, Philip; Kerr, Dermot; Coleman, SA; McGinnity, T.Martin.

Unknown Host Publication. 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Modelling Retinal Ganglion Cells Stimulated with Static Natural Images

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AU - Vance, Philip

AU - Kerr, Dermot

AU - Coleman, SA

AU - McGinnity, T.Martin

PY - 2016/3/24

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N2 - A standard approach to model retinal ganglion cells uses reverse correlation to construct a linear-nonlinear model using a cascade of a linear filter and a static nonlinearity. A major constraint with this technique is the need to use a radially symmetric stimulus, such as Gaussian white noise. Natural visual stimuli are required to generate a more realistic ganglion-cell model. However, natural visual stimuli significantly differ from white noise stimuli and are not radially symmetric. Therefore a more sophisticated modelling approach than the linear-nonlinear method is required for modelling ganglion cells stimulated with natural images. Machine learning algorithms have proved very capable in modelling complex non-linear systems in other scientific domains. In this paper, we report on the development of computational models, using different machine learning regression algorithms, that model retinal ganglion cells stimulated with natural images in order to predict the number of spikes elicited. Neuronal recordings obtained from electro-physiological experiments in which isolated salamander retinas are stimulated with static natural images are used to develop these models. In order to compare the performance of the machine learning models, a linear-nonlinear model was also developed from separate experiments using Gaussian white noise stimuli. A comparison of the spike prediction using the models developed shows that the machine learning models perform better than the linear-nonlinear approach.

AB - A standard approach to model retinal ganglion cells uses reverse correlation to construct a linear-nonlinear model using a cascade of a linear filter and a static nonlinearity. A major constraint with this technique is the need to use a radially symmetric stimulus, such as Gaussian white noise. Natural visual stimuli are required to generate a more realistic ganglion-cell model. However, natural visual stimuli significantly differ from white noise stimuli and are not radially symmetric. Therefore a more sophisticated modelling approach than the linear-nonlinear method is required for modelling ganglion cells stimulated with natural images. Machine learning algorithms have proved very capable in modelling complex non-linear systems in other scientific domains. In this paper, we report on the development of computational models, using different machine learning regression algorithms, that model retinal ganglion cells stimulated with natural images in order to predict the number of spikes elicited. Neuronal recordings obtained from electro-physiological experiments in which isolated salamander retinas are stimulated with static natural images are used to develop these models. In order to compare the performance of the machine learning models, a linear-nonlinear model was also developed from separate experiments using Gaussian white noise stimuli. A comparison of the spike prediction using the models developed shows that the machine learning models perform better than the linear-nonlinear approach.

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