A CNN framework for modeling parallel processing in a mammalian retina

Dávid Bálya, Botond Roska, T. Roska, Frank S. Werblin

Research output: Contribution to journalArticle

82 Citations (Scopus)

Abstract

We present here a simple multi-layer cellular neural/non-linear network (CNN) model of the mammalian retina, capable of implementation on CNN Universal Machine (CNN-UM) chips. The basis of the model is a simple multi-layer cellular neural/non-linear Network (IEEE Trans. Circuits Systems 1988; 35:1257; IEEE Trans. Circuits Systems 1993; 40:147). The characterization of the elements in the CNN model is based on anatomical and physiological studies performed in the rabbit retina. The living mammalian retina represents the visual world in a set of about a dozen different 'feature detecting' parallel representations (Nature 2001; 410:583-587). Our CNN model is capable of reproducing qualitatively the same full set of space-time patterns as the living retina in response to a flashed square. The modelling framework can then be used to predict the set of retinal responses to more complex patterns and is also applicable to studies of the other biological sensory systems. The work represents a major step forward in the complexity and programmability of retinal models.

Original languageEnglish
Pages (from-to)363-393
Number of pages31
JournalInternational Journal of Circuit Theory and Applications
Volume30
Issue number2-3
DOIs
Publication statusPublished - Mar 2002

Fingerprint

Nonlinear networks
Retina
Parallel Processing
Network Model
Nonlinear Model
Processing
Modeling
Multilayer
Networks (circuits)
Rabbit
Chip
Space-time
Framework
Predict
Model

Keywords

  • Cellular neural network
  • Inner retina
  • Mammalian retina
  • Multi-layer template
  • Programmable CNN-UM chip
  • Retina modelling
  • Spatial-temporal patterns

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

A CNN framework for modeling parallel processing in a mammalian retina. / Bálya, Dávid; Roska, Botond; Roska, T.; Werblin, Frank S.

In: International Journal of Circuit Theory and Applications, Vol. 30, No. 2-3, 03.2002, p. 363-393.

Research output: Contribution to journalArticle

Bálya, Dávid ; Roska, Botond ; Roska, T. ; Werblin, Frank S. / A CNN framework for modeling parallel processing in a mammalian retina. In: International Journal of Circuit Theory and Applications. 2002 ; Vol. 30, No. 2-3. pp. 363-393.
@article{eaa2f3d6303c49ba8b31f406149d2b9a,
title = "A CNN framework for modeling parallel processing in a mammalian retina",
abstract = "We present here a simple multi-layer cellular neural/non-linear network (CNN) model of the mammalian retina, capable of implementation on CNN Universal Machine (CNN-UM) chips. The basis of the model is a simple multi-layer cellular neural/non-linear Network (IEEE Trans. Circuits Systems 1988; 35:1257; IEEE Trans. Circuits Systems 1993; 40:147). The characterization of the elements in the CNN model is based on anatomical and physiological studies performed in the rabbit retina. The living mammalian retina represents the visual world in a set of about a dozen different 'feature detecting' parallel representations (Nature 2001; 410:583-587). Our CNN model is capable of reproducing qualitatively the same full set of space-time patterns as the living retina in response to a flashed square. The modelling framework can then be used to predict the set of retinal responses to more complex patterns and is also applicable to studies of the other biological sensory systems. The work represents a major step forward in the complexity and programmability of retinal models.",
keywords = "Cellular neural network, Inner retina, Mammalian retina, Multi-layer template, Programmable CNN-UM chip, Retina modelling, Spatial-temporal patterns",
author = "D{\'a}vid B{\'a}lya and Botond Roska and T. Roska and Werblin, {Frank S.}",
year = "2002",
month = "3",
doi = "10.1002/cta.204",
language = "English",
volume = "30",
pages = "363--393",
journal = "International Journal of Circuit Theory and Applications",
issn = "0098-9886",
publisher = "John Wiley and Sons Ltd",
number = "2-3",

}

TY - JOUR

T1 - A CNN framework for modeling parallel processing in a mammalian retina

AU - Bálya, Dávid

AU - Roska, Botond

AU - Roska, T.

AU - Werblin, Frank S.

PY - 2002/3

Y1 - 2002/3

N2 - We present here a simple multi-layer cellular neural/non-linear network (CNN) model of the mammalian retina, capable of implementation on CNN Universal Machine (CNN-UM) chips. The basis of the model is a simple multi-layer cellular neural/non-linear Network (IEEE Trans. Circuits Systems 1988; 35:1257; IEEE Trans. Circuits Systems 1993; 40:147). The characterization of the elements in the CNN model is based on anatomical and physiological studies performed in the rabbit retina. The living mammalian retina represents the visual world in a set of about a dozen different 'feature detecting' parallel representations (Nature 2001; 410:583-587). Our CNN model is capable of reproducing qualitatively the same full set of space-time patterns as the living retina in response to a flashed square. The modelling framework can then be used to predict the set of retinal responses to more complex patterns and is also applicable to studies of the other biological sensory systems. The work represents a major step forward in the complexity and programmability of retinal models.

AB - We present here a simple multi-layer cellular neural/non-linear network (CNN) model of the mammalian retina, capable of implementation on CNN Universal Machine (CNN-UM) chips. The basis of the model is a simple multi-layer cellular neural/non-linear Network (IEEE Trans. Circuits Systems 1988; 35:1257; IEEE Trans. Circuits Systems 1993; 40:147). The characterization of the elements in the CNN model is based on anatomical and physiological studies performed in the rabbit retina. The living mammalian retina represents the visual world in a set of about a dozen different 'feature detecting' parallel representations (Nature 2001; 410:583-587). Our CNN model is capable of reproducing qualitatively the same full set of space-time patterns as the living retina in response to a flashed square. The modelling framework can then be used to predict the set of retinal responses to more complex patterns and is also applicable to studies of the other biological sensory systems. The work represents a major step forward in the complexity and programmability of retinal models.

KW - Cellular neural network

KW - Inner retina

KW - Mammalian retina

KW - Multi-layer template

KW - Programmable CNN-UM chip

KW - Retina modelling

KW - Spatial-temporal patterns

UR - http://www.scopus.com/inward/record.url?scp=0036494199&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036494199&partnerID=8YFLogxK

U2 - 10.1002/cta.204

DO - 10.1002/cta.204

M3 - Article

VL - 30

SP - 363

EP - 393

JO - International Journal of Circuit Theory and Applications

JF - International Journal of Circuit Theory and Applications

SN - 0098-9886

IS - 2-3

ER -