An emergent model of orientation selectivity in cat visual cortical simple cells. 1995

D C Somers, and S B Nelson, and M Sur
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02139, USA.

It is well known that visual cortical neurons respond vigorously to a limited range of stimulus orientations, while their primary afferent inputs, neurons in the lateral geniculate nucleus (LGN), respond well to all orientations. Mechanisms based on intracortical inhibition and/or converging thalamocortical afferents have previously been suggested to underlie the generation of cortical orientation selectivity; however, these models conflict with experimental data. Here, a 1:4 scale model of a 1700 microns by 200 microms region of layer IV of cat primary visual cortex (area 17) is presented to demonstrate that local intracortical excitation may provide the dominant source of orientation-selective input. In agreement with experiment, model cortical cells exhibit sharp orientation selectivity despite receiving strong iso-orientation inhibition, weak cross-orientation inhibition, no shunting inhibition, and weakly tuned thalamocortical excitation. Sharp tuning is provided by recurrent cortical excitation. As this tuning signal arises from the same pool of neurons that it excites, orientation selectivity in the model is shown to be an emergent property of the cortical feedback circuitry. In the model, as in experiment, sharpness of orientation tuning is independent of stimulus contrast and persists with silencing of ON-type subfields. The model also provides a unified account of intracellular and extracellular inhibitory blockade experiments that had previously appeared to conflict over the role of inhibition. It is suggested that intracortical inhibition acts nonspecifically and indirectly to maintain the selectivity of individual neurons by balancing strong intracortical excitation at the columnar level.

UI MeSH Term Description Entries
D008959 Models, Neurological Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment. Neurologic Models,Model, Neurological,Neurologic Model,Neurological Model,Neurological Models,Model, Neurologic,Models, Neurologic
D009433 Neural Inhibition The function of opposing or restraining the excitation of neurons or their target excitable cells. Inhibition, Neural
D009474 Neurons The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM. Nerve Cells,Cell, Nerve,Cells, Nerve,Nerve Cell,Neuron
D012016 Reference Values The range or frequency distribution of a measurement in a population (of organisms, organs or things) that has not been selected for the presence of disease or abnormality. Normal Range,Normal Values,Reference Ranges,Normal Ranges,Normal Value,Range, Normal,Range, Reference,Ranges, Normal,Ranges, Reference,Reference Range,Reference Value,Value, Normal,Value, Reference,Values, Normal,Values, Reference
D002415 Cats The domestic cat, Felis catus, of the carnivore family FELIDAE, comprising over 30 different breeds. The domestic cat is descended primarily from the wild cat of Africa and extreme southwestern Asia. Though probably present in towns in Palestine as long ago as 7000 years, actual domestication occurred in Egypt about 4000 years ago. (From Walker's Mammals of the World, 6th ed, p801) Felis catus,Felis domesticus,Domestic Cats,Felis domestica,Felis sylvestris catus,Cat,Cat, Domestic,Cats, Domestic,Domestic Cat
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D000613 Aminobutyrates Derivatives of BUTYRIC ACID that contain one or more amino groups attached to the aliphatic structure. Included under this heading are a broad variety of acid forms, salts, esters, and amides that include the aminobutryrate structure. Aminobutyric Acids,Aminobutyric Acid,Acid, Aminobutyric,Acids, Aminobutyric
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia
D001640 Bicuculline An isoquinoline alkaloid obtained from Dicentra cucullaria and other plants. It is a competitive antagonist for GABA-A receptors. 6-(5,6,7,8-Tetrahydro-6-methyl-1,3-dioxolo(4,5-g)isoquinolin-5-yl)furo(3,4-e)1,3-benzodioxol-8(6H)one
D014793 Visual Cortex Area of the OCCIPITAL LOBE concerned with the processing of visual information relayed via VISUAL PATHWAYS. Area V2,Area V3,Area V4,Area V5,Associative Visual Cortex,Brodmann Area 18,Brodmann Area 19,Brodmann's Area 18,Brodmann's Area 19,Cortical Area V2,Cortical Area V3,Cortical Area V4,Cortical Area V5,Secondary Visual Cortex,Visual Cortex Secondary,Visual Cortex V2,Visual Cortex V3,Visual Cortex V3, V4, V5,Visual Cortex V4,Visual Cortex V5,Visual Cortex, Associative,Visual Motion Area,Extrastriate Cortex,Area 18, Brodmann,Area 18, Brodmann's,Area 19, Brodmann,Area 19, Brodmann's,Area V2, Cortical,Area V3, Cortical,Area V4, Cortical,Area V5, Cortical,Area, Visual Motion,Associative Visual Cortices,Brodmanns Area 18,Brodmanns Area 19,Cortex Secondary, Visual,Cortex V2, Visual,Cortex V3, Visual,Cortex, Associative Visual,Cortex, Extrastriate,Cortex, Secondary Visual,Cortex, Visual,Cortical Area V3s,Extrastriate Cortices,Secondary Visual Cortices,V3, Cortical Area,V3, Visual Cortex,V4, Area,V4, Cortical Area,V5, Area,V5, Cortical Area,V5, Visual Cortex,Visual Cortex Secondaries,Visual Cortex, Secondary,Visual Motion Areas

Related Publications

D C Somers, and S B Nelson, and M Sur
March 1996, Nature,
D C Somers, and S B Nelson, and M Sur
April 2015, Physiological reports,
D C Somers, and S B Nelson, and M Sur
November 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience,
D C Somers, and S B Nelson, and M Sur
November 1996, The Journal of physiology,
D C Somers, and S B Nelson, and M Sur
February 1992, Neuroreport,
D C Somers, and S B Nelson, and M Sur
November 1975, Science (New York, N.Y.),
D C Somers, and S B Nelson, and M Sur
August 2018, Cell reports,
D C Somers, and S B Nelson, and M Sur
June 1987, The Journal of neuroscience : the official journal of the Society for Neuroscience,
D C Somers, and S B Nelson, and M Sur
July 2005, IEEE transactions on neural networks,
Copied contents to your clipboard!