Modelling and Analysis of Retinal Ganglion Cells with Neural Networks

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

57 Citations (Scopus)

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 neural network techniques we model the highly complex neuronal structures of visual processing retinal cells and represent the mapping between perception and response automatically
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages95-100
Number of pages6
Publication statusPublished - 27 Aug 2014
EventIrish Machine Vision and Image Processing 2014 -
Duration: 27 Aug 2014 → …

Conference

ConferenceIrish Machine Vision and Image Processing 2014
Period27/08/14 → …

Fingerprint

Neural networks
Neurons
Biological systems
Cells
Processing

Cite this

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title = "Modelling and Analysis of Retinal Ganglion Cells with Neural Networks",
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 neural network techniques we model the highly complex neuronal structures of visual processing retinal cells and represent the mapping between perception and response automatically",
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Kerr, D, Coleman, SA & McGinnity, TM 2014, Modelling and Analysis of Retinal Ganglion Cells with Neural Networks. in Unknown Host Publication. pp. 95-100, Irish Machine Vision and Image Processing 2014, 27/08/14.

Modelling and Analysis of Retinal Ganglion Cells with Neural Networks. / Kerr, D; Coleman, SA; McGinnity, TM.

Unknown Host Publication. 2014. p. 95-100.

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

TY - GEN

T1 - Modelling and Analysis of Retinal Ganglion Cells with Neural Networks

AU - Kerr, D

AU - Coleman, SA

AU - McGinnity, TM

PY - 2014/8/27

Y1 - 2014/8/27

N2 - 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 neural network techniques we model the highly complex neuronal structures of visual processing retinal cells and represent the mapping between perception and response automatically

AB - 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 neural network techniques we model the highly complex neuronal structures of visual processing retinal cells and represent the mapping between perception and response automatically

M3 - Conference contribution

SP - 95

EP - 100

BT - Unknown Host Publication

ER -