@article{a382b8d94a824b4396f69ff6a1af9f5d,
title = "Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity",
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.",
author = "Roxana Zeraati and Shi, {Yan Liang} and Steinmetz, {Nicholas A.} and Gieselmann, {Marc A.} and Alexander Thiele and Tirin Moore and Anna Levina and Engel, {Tatiana A.}",
note = "Funding Information: This work was supported by a Sofja Kovalevskaja Award from the Alexander von Humboldt Foundation, endowed by the Federal Ministry of Education and Research (R.Z., A.L.), SMARTSTART2 program provided by Bernstein Center for Computational Neuroscience and Volkswagen Foundation (R.Z.), the NIH grant R01 EB026949 (T.A.E.), the Swartz Foundation (Y.S.), the Pershing Square Foundation (T.A.E.), the Sloan Research Fellowship (Y.S., T.A.E.), NIH grant RF1DA055666 (Y.S., T.A.E.), the NIH grant EY014924 (T.M.), the MRC grant MR/P013031/1 (M.A.G., A.T.). This work was performed with assistance from the NIH Grant S10OD028632-0. We acknowledge the support from the BMBF through the T{\"u}bingen AI Center (FKZ: 01IS18039B) and the International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction (IMPRS-MMFD). A.L. is a member of the Machine Learning Cluster of Excellence, EXC number 2064/1 - Project number 39072764. We thank Julia Wang for providing the code for estimating receptive fields. Funding Information: This work was supported by a Sofja Kovalevskaja Award from the Alexander von Humboldt Foundation, endowed by the Federal Ministry of Education and Research (R.Z., A.L.), SMARTSTART2 program provided by Bernstein Center for Computational Neuroscience and Volkswagen Foundation (R.Z.), the NIH grant R01 EB026949 (T.A.E.), the Swartz Foundation (Y.S.), the Pershing Square Foundation (T.A.E.), the Sloan Research Fellowship (Y.S., T.A.E.), NIH grant RF1DA055666 (Y.S., T.A.E.), the NIH grant EY014924 (T.M.), the MRC grant MR/P013031/1 (M.A.G., A.T.). This work was performed with assistance from the NIH Grant S10OD028632-0. We acknowledge the support from the BMBF through the T{\"u}bingen AI Center (FKZ: 01IS18039B) and the International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction (IMPRS-MMFD). A.L. is a member of the Machine Learning Cluster of Excellence, EXC number 2064/1 - Project number 39072764. We thank Julia Wang for providing the code for estimating receptive fields. Publisher Copyright: {\textcopyright} 2023, The Author(s).",
year = "2023",
month = dec,
doi = "10.1038/s41467-023-37613-7",
language = "English (US)",
volume = "14",
journal = "Nature communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",
}