The evolution of large-scale modeling of monkey primary visual cortex, V1: steps towards understanding cortical function

Louis Tao (Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing, China; and Center for Quantitative Biology, Peking University, Beijing, China)

Michael Shelley (Courant Institute of Mathematical Sciences, New York University, New York, N.Y., U.S.A.)

Robert Shapley (Center for Neural Science, Neuroscience Institute, and Courant Institute of Mathematical Sciences, New York University, New York, N.Y., U.S.A.)

Aaditya Rangan (Courant Institute of Mathematical Sciences, New York University, New York, N.Y., U.S.A.)

David W. McLaughlin (Courant Institute of Mathematical Science, Center for Neural Science, Tandon School of Engineering, and the Neuroscience Institute at Langone Medical Center, New York University, New York, N.Y., U.S.A.)

Abstract

Over the past two decades, mathematicians and neuroscientists at New York University have developed several large-scale computational models of a layer of macaque primary visual cortex. Here we provide an overview of these models, organized by the specific questions about cortical processing that each model addressed. Each model was founded upon the available anatomical and physiological data; and not by building into the model network assumptions about theoretical mechanisms specifically designed to enable the network to produce desired response properties. Also, our aim was to use one comprehensive network, with a fixed architecture and one set of parameters, to model all experiments. The response properties of individual neurons and populations of neurons then emerge from this experimentally constrained model. This overview is dedicated to Professor David Cai, who played a leading role in several of the models described here. We are very fortunate to have had the opportunity to work with him over the past two decades.

Keywords

visual neural science, computational modeling, orientation tuning

2010 Mathematics Subject Classification

60F10

Full Text (PDF format)

Received 2 April 2019

Accepted 6 September 2019

Published 6 December 2019