Modelling Retinal Ganglion Cells using Self-Organising Fuzzy Neural Networks

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

4 Citations (Scopus)

Abstract

Even though artificial vision has been in development for over half a century it still fares poorly when compared to biological vision. The processing capabilities of biological visual systems are vastly superior in terms of power, speed, and performance. Inspired by this robust performance artificial vision systems have sought to take inspiration from biology by modeling aspects of biological vision systems. 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. In this work we use state-of-the-art fuzzy neural network techniques to accurately model the responses of retinal ganglion cells. We illustrate how a self-organising fuzzy neural network can accurately model ganglion cell behaviour, and are a viable alternative to traditional system identification techniques.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages1-8
Number of pages8
DOIs
Publication statusPublished - 12 Jul 2015
Event2015 International Joint Conference on Neural Networks (IJCNN), - Killarney
Duration: 12 Jul 2015 → …

Conference

Conference2015 International Joint Conference on Neural Networks (IJCNN),
Period12/07/15 → …

Fingerprint

Fuzzy neural networks
Computer vision
Neurons
Identification (control systems)
Processing

Keywords

  • modelling
  • retinal ganglion cells

Cite this

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title = "Modelling Retinal Ganglion Cells using Self-Organising Fuzzy Neural Networks",
abstract = "Even though artificial vision has been in development for over half a century it still fares poorly when compared to biological vision. The processing capabilities of biological visual systems are vastly superior in terms of power, speed, and performance. Inspired by this robust performance artificial vision systems have sought to take inspiration from biology by modeling aspects of biological vision systems. 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. In this work we use state-of-the-art fuzzy neural network techniques to accurately model the responses of retinal ganglion cells. We illustrate how a self-organising fuzzy neural network can accurately model ganglion cell behaviour, and are a viable alternative to traditional system identification techniques.",
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author = "Scott McDonald and D Kerr and SA Coleman and Philip Vance and Martin McGinnity",
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McDonald, S, Kerr, D, Coleman, SA, Vance, P & McGinnity, M 2015, Modelling Retinal Ganglion Cells using Self-Organising Fuzzy Neural Networks. in Unknown Host Publication. pp. 1-8, 2015 International Joint Conference on Neural Networks (IJCNN), 12/07/15. https://doi.org/10.1109/IJCNN.2015.7280697

Modelling Retinal Ganglion Cells using Self-Organising Fuzzy Neural Networks. / McDonald, Scott; Kerr, D; Coleman, SA; Vance, Philip; McGinnity, Martin.

Unknown Host Publication. 2015. p. 1-8.

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

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