Stochastic and synchronous neural circuit dynamics underlying feature-similarity gain modulation

Stochastic and synchronous neural circuit dynamics underlying feature-similarity gain modulation Xiao-Jing Wang With Salvador Ardid and Albert Compte

A recurrent network mechanism for working memory Compte, Brunel, Goldman-Rakic and Wang 2000

A network model of PFC/PPC-MT loop Compte et al Cereb Cortex 2000 Ardid et al J Neurosci 2007

Calibrate MT network:normalization by strong recurrent inhibition Input coming from V1 MT model output Snowden, Treue, Erickson, Andersen 1991; Heeger 1992

no-attention + + + A Att S No-Att 2sec attention + t + + 55Hz Neural label  PFC MT

Att no Att Network activity pattern shows selective enhancement Neural label 

Multiplicative gain modulation of tuning curve MT model neuron response scaled responses Att: pref Att: nonpref Att: at 90

Martinez-Trujillo and Treue (Current Biol 2004)

Network pattern in a single trial MT activity when A=S are covaried Att no Att Neural label  Martinez-Trujillo and Treue, 2004 R(,S,A)=G(A-) R0(-S)

example MT cell Population data Martinez-Trujillo and Treue, 2004 Modulation ratio

Feature-similarity R(-S,-A)=G(A-) R0(-S) G(A-)= 1+g0 cos(A-) R(,S,A)=R(-S,-A,A-S) Multiplicative scaling of single cell’s tuning curve But selective enhancement of population activity profile in single trials

+ + + 2sec Transparent motion with attention Without attention + + + 55Hz PFC MT

Att no Att Neural label Network Activity pattern R(,S,A)=G(A-) R0(-S)

Att no Att prediction Neural label Feature-similarity gain principle holds for transparent motion R(-S,-A)=G(A-) R0(-S)

Biased competition 45Hz

Stochastic and synchronous neural circuit dynamics underlying feature-similarity gain modulation