Bioinspired approach to modeling retinal ganglion cells using system identification techniques

Philip J. Vance*, Gautham P. Das, Dermot Kerr, Sonya A. Coleman, T. Martin McGinnity, Tim Gollisch, Jian K. Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 modeling the human visual system is the ability to accurately model the behavior and computation within the retina. In particular, we focus on modeling the retinal ganglion cells (RGCs) 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 RGCs can be derived by quantitatively fitting the 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 modeled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this paper, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behavior, and are a viable alternative to traditional linear-nonlinear approaches.

Original languageEnglish
Pages (from-to)1796-1808
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number5
Publication statusPublished - 1 May 2018


  • Artificial stimuli
  • Biological vision
  • Computational modeling
  • Receptive field (RF)
  • Retinal ganglion cells (RGCs)

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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