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Modelling Retinal Ganglion Cells using Self-Organising Fuzzy Neural Networks

McDonald, Scott, Kerr, D, Coleman, SA, Vance, Philip and McGinnity, Martin (2015) Modelling Retinal Ganglion Cells using Self-Organising Fuzzy Neural Networks. In: 2015 International Joint Conference on Neural Networks (IJCNN),, Killarney. IEEE. 8 pp. [Conference contribution]

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DOI: 10.1109/IJCNN.2015.7280697


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.

Item Type:Conference contribution (Paper)
Keywords:modelling, retinal ganglion cells
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:32523
Deposited By: Dr Sonya Coleman
Deposited On:04 Nov 2015 11:42
Last Modified:04 Nov 2015 11:42

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