Abstract

Intrinsic timescales characterize dynamics of endogenous fluctuations in neural activity. Variation of intrinsic timescales across the neocortex reflects functional specialization of cortical areas, but less is known about how intrinsic timescales change during cognitive tasks. We measured intrinsic timescales of local spiking activity within columns of area V4 in male monkeys performing spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales, fast and slow. The slow timescale increased when monkeys attended to the receptive fields location and correlated with reaction times. By evaluating predictions of several network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and attentional modulation of timescales results from an increase in the efficacy of recurrent interactions. Our results suggest that multiple timescales may arise from the spatial connectivity in the visual cortex and flexibly change with the cognitive state due to dynamic effective interactions between neurons.

Not much is known about how intrinsic timescales, which characterize the dynamics of endogenous fluctuations in neural activity, change during cognitive tasks. Here, the authors show that intrinsic timescales of neural activity in the primate visual cortex change during spatial attention. Experimental data were best explained by a network model in which timescales arise from spatially arranged connectivity.

Details

Title
Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity
Author
Zeraati, Roxana 1   VIAFID ORCID Logo  ; Shi, Yan-Liang 2   VIAFID ORCID Logo  ; Steinmetz, Nicholas A. 3   VIAFID ORCID Logo  ; Gieselmann, Marc A. 4 ; Thiele, Alexander 4 ; Moore, Tirin 5   VIAFID ORCID Logo  ; Levina, Anna 6   VIAFID ORCID Logo  ; Engel, Tatiana A. 2   VIAFID ORCID Logo 

 University of Tübingen, International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, Tübingen, Germany (GRID:grid.10392.39) (ISNI:0000 0001 2190 1447); Max Planck Institute for Biological Cybernetics, Tübingen, Germany (GRID:grid.419501.8) (ISNI:0000 0001 2183 0052) 
 Cold Spring Harbor Laboratory, Cold Spring Harbor, USA (GRID:grid.225279.9) (ISNI:0000 0004 0387 3667); Princeton University, Princeton Neuroscience Institute, Princeton, USA (GRID:grid.16750.35) (ISNI:0000 0001 2097 5006) 
 University of Washington, Department of Biological Structure, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) 
 Newcastle University, Biosciences Institute, Newcastle upon Tyne, UK (GRID:grid.1006.7) (ISNI:0000 0001 0462 7212) 
 Stanford University, Department of Neurobiology and Howard Hughes Medical Institute, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956) 
 Max Planck Institute for Biological Cybernetics, Tübingen, Germany (GRID:grid.419501.8) (ISNI:0000 0001 2183 0052); University of Tübingen, Department of Computer Science, Tübingen, Germany (GRID:grid.10392.39) (ISNI:0000 0001 2190 1447); Bernstein Center for Computational Neuroscience Tübingen, Tübingen, Germany (GRID:grid.455094.9) 
Pages
1858
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2794408166
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.