How simple cells are made in a nonlinear network model of the visual cortex

D. J. Wielaard, M. Shelley, D. McLaughlin, R. Shapley

Research output: Contribution to journalArticle

Abstract

Simple cells in the striate cortex respond to visual stimuli in an approximately linear manner, although the LGN input to the striate cortex, and the cortical network itself, are highly nonlinear. Although simple cells are vital for visual perception, there has been no satisfactory explanation of how they are produced in the cortex. To examine this question, we have developed a large-scale neuronal network model of layer 4Cα in V1 of the macaque cortex that is based on, and constrained by, realistic cortical anatomy and physiology. This paper has two aims: (1) to show that neurons in the model respond like simple cells. (2) To identify how the model generates this linearized response in a nonlinear network. Each neuron in the model receives nonlinear excitation from the lateral geniculate nucleus (LGN). The cells of the model receive strong (nonlinear) lateral inhibition from other neurons in the model cortex. Mathematical analysis of the dependence of membrane potential on synaptic conductances, and computer simulations, reveal that the nonlinearity of corticocortical inhibition cancels the nonlinear excitatory input from the LGN. This interaction produces linearized responses that agree with both extracellular and intracellular measurements. The model correctly accounts for experimental results about the time course of simple cell responses and also generates testable predictions about variation in linearity with position in the cortex, and the effect on the linearity of signal summation, caused by unbalancing the relative strengths of excitation and inhibition pharmacologically or with extrinsic current.

Original languageEnglish (US)
Pages (from-to)5203-5211
Number of pages9
JournalJournal of Neuroscience
Volume21
Issue number14
StatePublished - Jul 15 2001

Fingerprint

Nonlinear Dynamics
Visual Cortex
Geniculate Bodies
Neurons
Visual Perception
Macaca
Computer Simulation
Membrane Potentials
Anatomy
Inhibition (Psychology)

Keywords

  • Linearity
  • Neuronal network model
  • Phase averaging
  • Primary visual cortex
  • Simple cells
  • Synaptic inhibition

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

How simple cells are made in a nonlinear network model of the visual cortex. / Wielaard, D. J.; Shelley, M.; McLaughlin, D.; Shapley, R.

In: Journal of Neuroscience, Vol. 21, No. 14, 15.07.2001, p. 5203-5211.

Research output: Contribution to journalArticle

@article{dc628f9bb35c4e599b70e012e73c2b61,
title = "How simple cells are made in a nonlinear network model of the visual cortex",
abstract = "Simple cells in the striate cortex respond to visual stimuli in an approximately linear manner, although the LGN input to the striate cortex, and the cortical network itself, are highly nonlinear. Although simple cells are vital for visual perception, there has been no satisfactory explanation of how they are produced in the cortex. To examine this question, we have developed a large-scale neuronal network model of layer 4Cα in V1 of the macaque cortex that is based on, and constrained by, realistic cortical anatomy and physiology. This paper has two aims: (1) to show that neurons in the model respond like simple cells. (2) To identify how the model generates this linearized response in a nonlinear network. Each neuron in the model receives nonlinear excitation from the lateral geniculate nucleus (LGN). The cells of the model receive strong (nonlinear) lateral inhibition from other neurons in the model cortex. Mathematical analysis of the dependence of membrane potential on synaptic conductances, and computer simulations, reveal that the nonlinearity of corticocortical inhibition cancels the nonlinear excitatory input from the LGN. This interaction produces linearized responses that agree with both extracellular and intracellular measurements. The model correctly accounts for experimental results about the time course of simple cell responses and also generates testable predictions about variation in linearity with position in the cortex, and the effect on the linearity of signal summation, caused by unbalancing the relative strengths of excitation and inhibition pharmacologically or with extrinsic current.",
keywords = "Linearity, Neuronal network model, Phase averaging, Primary visual cortex, Simple cells, Synaptic inhibition",
author = "Wielaard, {D. J.} and M. Shelley and D. McLaughlin and R. Shapley",
year = "2001",
month = "7",
day = "15",
language = "English (US)",
volume = "21",
pages = "5203--5211",
journal = "Journal of Neuroscience",
issn = "0270-6474",
publisher = "Society for Neuroscience",
number = "14",

}

TY - JOUR

T1 - How simple cells are made in a nonlinear network model of the visual cortex

AU - Wielaard, D. J.

AU - Shelley, M.

AU - McLaughlin, D.

AU - Shapley, R.

PY - 2001/7/15

Y1 - 2001/7/15

N2 - Simple cells in the striate cortex respond to visual stimuli in an approximately linear manner, although the LGN input to the striate cortex, and the cortical network itself, are highly nonlinear. Although simple cells are vital for visual perception, there has been no satisfactory explanation of how they are produced in the cortex. To examine this question, we have developed a large-scale neuronal network model of layer 4Cα in V1 of the macaque cortex that is based on, and constrained by, realistic cortical anatomy and physiology. This paper has two aims: (1) to show that neurons in the model respond like simple cells. (2) To identify how the model generates this linearized response in a nonlinear network. Each neuron in the model receives nonlinear excitation from the lateral geniculate nucleus (LGN). The cells of the model receive strong (nonlinear) lateral inhibition from other neurons in the model cortex. Mathematical analysis of the dependence of membrane potential on synaptic conductances, and computer simulations, reveal that the nonlinearity of corticocortical inhibition cancels the nonlinear excitatory input from the LGN. This interaction produces linearized responses that agree with both extracellular and intracellular measurements. The model correctly accounts for experimental results about the time course of simple cell responses and also generates testable predictions about variation in linearity with position in the cortex, and the effect on the linearity of signal summation, caused by unbalancing the relative strengths of excitation and inhibition pharmacologically or with extrinsic current.

AB - Simple cells in the striate cortex respond to visual stimuli in an approximately linear manner, although the LGN input to the striate cortex, and the cortical network itself, are highly nonlinear. Although simple cells are vital for visual perception, there has been no satisfactory explanation of how they are produced in the cortex. To examine this question, we have developed a large-scale neuronal network model of layer 4Cα in V1 of the macaque cortex that is based on, and constrained by, realistic cortical anatomy and physiology. This paper has two aims: (1) to show that neurons in the model respond like simple cells. (2) To identify how the model generates this linearized response in a nonlinear network. Each neuron in the model receives nonlinear excitation from the lateral geniculate nucleus (LGN). The cells of the model receive strong (nonlinear) lateral inhibition from other neurons in the model cortex. Mathematical analysis of the dependence of membrane potential on synaptic conductances, and computer simulations, reveal that the nonlinearity of corticocortical inhibition cancels the nonlinear excitatory input from the LGN. This interaction produces linearized responses that agree with both extracellular and intracellular measurements. The model correctly accounts for experimental results about the time course of simple cell responses and also generates testable predictions about variation in linearity with position in the cortex, and the effect on the linearity of signal summation, caused by unbalancing the relative strengths of excitation and inhibition pharmacologically or with extrinsic current.

KW - Linearity

KW - Neuronal network model

KW - Phase averaging

KW - Primary visual cortex

KW - Simple cells

KW - Synaptic inhibition

UR - http://www.scopus.com/inward/record.url?scp=0035879172&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035879172&partnerID=8YFLogxK

M3 - Article

VL - 21

SP - 5203

EP - 5211

JO - Journal of Neuroscience

JF - Journal of Neuroscience

SN - 0270-6474

IS - 14

ER -