TY - JOUR
T1 - The evolution of large-scale modeling of monkey primary visual cortex, V1
T2 - Steps towards understanding cortical function
AU - Young, Lai Sang
AU - Tao, Louis
AU - Shelley, Michael
AU - Shapley, Robert
AU - Rangan, Aaditya
AU - Mclaughlin, David W.
N1 - Publisher Copyright:
© 2019 International Press.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Computational modeling
KW - Orientation tuning
KW - Visual neural science
UR - http://www.scopus.com/inward/record.url?scp=85077471462&partnerID=8YFLogxK
U2 - 10.4310/CMS.2019.v17.n5.a10
DO - 10.4310/CMS.2019.v17.n5.a10
M3 - Article
AN - SCOPUS:85077471462
SN - 1539-6746
VL - 17
SP - 1387
EP - 1406
JO - Communications in Mathematical Sciences
JF - Communications in Mathematical Sciences
IS - 5
ER -