Bio-Inspired Approach to Modelling Retinal Ganglion Cells using System Identification Techniques

Philip Vance, Gautham Das, Dermot Kerr, Sonya Coleman, T.Martin McGinnity, Tim Gollisch, Jian Liu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power and performance. A key aspect to modelling the human visual system is the ability to accurately model the behaviour and computation within the retina. In particular, we focus on modelling the retinal ganglion cells as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within retinal ganglion cells can be derived by quantitatively fitting sets of physiological data using an input-output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input-output responses are modelled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this work, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behaviour, and are a viable alternative to traditional linear-nonlinear approaches.
LanguageEnglish
Pages1796-1808
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume29
Issue number5
Early online date12 Apr 2017
DOIs
Publication statusPublished - May 2018

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Identification (control systems)
Computer vision
Biophysics
Biological systems
Processing
Optics
Animals

Keywords

  • Retinal ganglion cells
  • computational modelling
  • biological vision
  • receptive field
  • artificial stimuli.

Cite this

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abstract = "The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power and performance. A key aspect to modelling the human visual system is the ability to accurately model the behaviour and computation within the retina. In particular, we focus on modelling the retinal ganglion cells as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within retinal ganglion cells can be derived by quantitatively fitting sets of physiological data using an input-output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input-output responses are modelled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this work, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behaviour, and are a viable alternative to traditional linear-nonlinear approaches.",
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