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Authors: Dermot Kerr 1 ; Martin McGinnity 2 and Sonya Coleman 1

Affiliations: 1 University of Ulster, United Kingdom ; 2 Nottingham Trent University, United Kingdom

ISBN: 978-989-758-054-3

Keyword(s): System Identification, Retinal Ganglion Cells, Linear-Nonlinear Model.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Bio-Inspired and Humanoid Robotics ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computational Neuroscience ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image Processing and Artificial Vision Applications ; Intelligent Artificial Perception and Neural Sensors ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neuroinformatics and Bioinformatics ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Modelling biological systems is difficult due to insufficient knowledge about the internal components and organisation, and the complexity of the interactions within the system. At cellular level existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. At a computational level we aspire to take advantage of state-of-the-art techniques to accurately model non-standard types of retinal ganglion cells. Using system identification techniques to express the biological input-output coupling mathematically, and computational modelling techniques to model highly complex neuronal structures, we will "identify" ganglion cell behaviour with visual scenes, and represent the mapping between perception and respo nse automatically. (More)

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Paper citation in several formats:
Kerr, D.; McGinnity, M. and Coleman, S. (2014). Modelling and Analysis of Retinal Ganglion Cells Through System Identification.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 158-164. DOI: 10.5220/0005069701580164

@conference{ncta14,
author={Dermot Kerr. and Martin McGinnity. and Sonya Coleman.},
title={Modelling and Analysis of Retinal Ganglion Cells Through System Identification},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={158-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005069701580164},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Modelling and Analysis of Retinal Ganglion Cells Through System Identification
SN - 978-989-758-054-3
AU - Kerr, D.
AU - McGinnity, M.
AU - Coleman, S.
PY - 2014
SP - 158
EP - 164
DO - 10.5220/0005069701580164

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