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

Research output: Contribution to journalArticleResearchpeer-review

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 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
Volume29
Issue number5
DOIs
Publication statusPublished - 1 May 2018

Fingerprint

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

Keywords

  • 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

Cite this

Bioinspired approach to modeling retinal ganglion cells using system identification techniques. / Vance, Philip J.; Das, Gautham P.; Kerr, Dermot; Coleman, Sonya A.; McGinnity, T. Martin; Gollisch, Tim; Liu, Jian K.

In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, No. 5, 01.05.2018, p. 1796-1808.

Research output: Contribution to journalArticleResearchpeer-review

Vance, Philip J. ; Das, Gautham P. ; Kerr, Dermot ; Coleman, Sonya A. ; McGinnity, T. Martin ; Gollisch, Tim ; Liu, Jian K. / Bioinspired approach to modeling retinal ganglion cells using system identification techniques. In: IEEE Transactions on Neural Networks and Learning Systems. 2018 ; Vol. 29, No. 5. pp. 1796-1808.
@article{afbb58bb41c04956bae4d1dc6392b0ba,
title = "Bioinspired approach to modeling retinal ganglion cells using system identification techniques",
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 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.",
keywords = "Artificial stimuli, Biological vision, Computational modeling, Receptive field (RF), Retinal ganglion cells (RGCs)",
author = "Vance, {Philip J.} and Das, {Gautham P.} and Dermot Kerr and Coleman, {Sonya A.} and McGinnity, {T. Martin} and Tim Gollisch and Liu, {Jian K.}",
year = "2018",
month = "5",
day = "1",
doi = "10.1109/TNNLS.2017.2690139",
language = "English",
volume = "29",
pages = "1796--1808",
journal = "IEEE Transactions on Neural Networks and Learning Systems",
issn = "2162-237X",
publisher = "Institute of Electrical and Electronics Engineers",
number = "5",

}

TY - JOUR

T1 - Bioinspired approach to modeling retinal ganglion cells using system identification techniques

AU - Vance, Philip J.

AU - Das, Gautham P.

AU - Kerr, Dermot

AU - Coleman, Sonya A.

AU - McGinnity, T. Martin

AU - Gollisch, Tim

AU - Liu, Jian K.

PY - 2018/5/1

Y1 - 2018/5/1

N2 - 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.

AB - 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.

KW - Artificial stimuli

KW - Biological vision

KW - Computational modeling

KW - Receptive field (RF)

KW - Retinal ganglion cells (RGCs)

UR - http://www.scopus.com/inward/record.url?scp=85018522246&partnerID=8YFLogxK

U2 - 10.1109/TNNLS.2017.2690139

DO - 10.1109/TNNLS.2017.2690139

M3 - Article

VL - 29

SP - 1796

EP - 1808

JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

SN - 2162-237X

IS - 5

ER -