TY - JOUR

T1 - A recurrent circuit implements normalization, simulating the dynamics of V1 activity

AU - Heeger, David J.

AU - Zemlianova, Klavdia O.

N1 - Publisher Copyright:
© 2020 National Academy of Sciences. All rights reserved.

PY - 2020/9/8

Y1 - 2020/9/8

N2 - The normalization model has been applied to explain neural activity in diverse neural systems including primary visual cortex (V1). The model's defining characteristic is that the response of each neuron is divided by a factor that includes a weighted sum of activity of a pool of neurons. Despite the success of the normalization model, there are three unresolved issues. 1) Experimental evidence supports the hypothesis that normalization in V1 operates via recurrent amplification, i.e., amplifying weak inputs more than strong inputs. It is unknown how normalization arises from recurrent amplification. 2) Experiments have demonstrated that normalization is weighted such that each weight specifies how one neuron contributes to another's normalization pool. It is unknown how weighted normalization arises from a recurrent circuit. 3) Neural activity in V1 exhibits complex dynamics, including gamma oscillations, linked to normalization. It is unknown how these dynamics emerge from normalization. Here, a family of recurrent circuit models is reported, each of which comprises coupled neural integrators to implement normalization via recurrent amplification with arbitrary normalization weights, some of which can recapitulate key experimental observations of the dynamics of neural activity in V1.

AB - The normalization model has been applied to explain neural activity in diverse neural systems including primary visual cortex (V1). The model's defining characteristic is that the response of each neuron is divided by a factor that includes a weighted sum of activity of a pool of neurons. Despite the success of the normalization model, there are three unresolved issues. 1) Experimental evidence supports the hypothesis that normalization in V1 operates via recurrent amplification, i.e., amplifying weak inputs more than strong inputs. It is unknown how normalization arises from recurrent amplification. 2) Experiments have demonstrated that normalization is weighted such that each weight specifies how one neuron contributes to another's normalization pool. It is unknown how weighted normalization arises from a recurrent circuit. 3) Neural activity in V1 exhibits complex dynamics, including gamma oscillations, linked to normalization. It is unknown how these dynamics emerge from normalization. Here, a family of recurrent circuit models is reported, each of which comprises coupled neural integrators to implement normalization via recurrent amplification with arbitrary normalization weights, some of which can recapitulate key experimental observations of the dynamics of neural activity in V1.

KW - Computational neuroscience

KW - Gamma oscillation

KW - Normalization

KW - Recurrent neural network

KW - V1

UR - http://www.scopus.com/inward/record.url?scp=85090614467&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85090614467&partnerID=8YFLogxK

U2 - 10.1073/pnas.2005417117

DO - 10.1073/pnas.2005417117

M3 - Article

C2 - 32843341

AN - SCOPUS:85090614467

SN - 0027-8424

VL - 117

SP - 22494

EP - 22505

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

IS - 36

ER -