Modelling and Analysis of Retinal Ganglion Cells Through System Identification

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

4 Citations (Scopus)
25 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherSciTePress
Pages158-164
Number of pages7
ISBN (Print)978-989-758-054-3
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 → …

Keywords

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

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