Modelling and Analysis of Retinal Ganglion Cells Through System Identification

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

4 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 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 response automatically.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages158-164
Number of pages7
DOIs
Publication statusPublished - 2014
EventInternational Conference on Neural Computation Theory and Applications (NCTA 2014) - Rome, Italy
Duration: 1 Jan 2014 → …

Conference

ConferenceInternational Conference on Neural Computation Theory and Applications (NCTA 2014)
Period1/01/14 → …

Fingerprint

Identification (control systems)
Neurons
Biological systems

Keywords

  • System Identification
  • Retinal Ganglion Cells
  • Linear-Nonlinear Model

Cite this

@inproceedings{73ff1c2de38b4d42bfcace63ad307afb,
title = "Modelling and Analysis of Retinal Ganglion Cells Through System Identification",
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 response automatically.",
keywords = "System Identification, Retinal Ganglion Cells, Linear-Nonlinear Model",
author = "Dermot Kerr and Martin McGinnity and SA Coleman",
year = "2014",
doi = "10.5220/0005069701580164",
language = "English",
isbn = "978-989-758-054-3",
pages = "158--164",
booktitle = "Unknown Host Publication",

}

Kerr, D, McGinnity, M & Coleman, SA 2014, Modelling and Analysis of Retinal Ganglion Cells Through System Identification. in Unknown Host Publication. pp. 158-164, International Conference on Neural Computation Theory and Applications (NCTA 2014), 1/01/14. https://doi.org/10.5220/0005069701580164

Modelling and Analysis of Retinal Ganglion Cells Through System Identification. / Kerr, Dermot; McGinnity, Martin; Coleman, SA.

Unknown Host Publication. 2014. p. 158-164.

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

TY - GEN

T1 - Modelling and Analysis of Retinal Ganglion Cells Through System Identification

AU - Kerr, Dermot

AU - McGinnity, Martin

AU - Coleman, SA

PY - 2014

Y1 - 2014

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 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 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 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 response automatically.

KW - System Identification

KW - Retinal Ganglion Cells

KW - Linear-Nonlinear Model

U2 - 10.5220/0005069701580164

DO - 10.5220/0005069701580164

M3 - Conference contribution

SN - 978-989-758-054-3

SP - 158

EP - 164

BT - Unknown Host Publication

ER -