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On the Complexity of Resting State Spiking Activity in Monkey Motor Cortex.


ABSTRACT: Resting state has been established as a classical paradigm of brain activity studies, mostly based on large-scale measurements such as functional magnetic resonance imaging or magneto- and electroencephalography. This term typically refers to a behavioral state characterized by the absence of any task or stimuli. The corresponding neuronal activity is often called idle or ongoing. Numerous modeling studies on spiking neural networks claim to mimic such idle states, but compare their results with task- or stimulus-driven experiments, or to results from experiments with anesthetized subjects. Both approaches might lead to misleading conclusions. To provide a proper basis for comparing physiological and simulated network dynamics, we characterize simultaneously recorded single neurons' spiking activity in monkey motor cortex at rest and show the differences from spontaneous and task- or stimulus-induced movement conditions. We also distinguish between rest with open eyes and sleepy rest with eyes closed. The resting state with open eyes shows a significantly higher dimensionality, reduced firing rates, and less balance between population level excitation and inhibition than behavior-related states.

SUBMITTER: Dabrowska PA 

PROVIDER: S-EPMC8271144 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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On the Complexity of Resting State Spiking Activity in Monkey Motor Cortex.

Dąbrowska Paulina Anna PA   Voges Nicole N   von Papen Michael M   Ito Junji J   Dahmen David D   Riehle Alexa A   Brochier Thomas T   Grün Sonja S  

Cerebral cortex communications 20210518 3


Resting state has been established as a classical paradigm of brain activity studies, mostly based on large-scale measurements such as functional magnetic resonance imaging or magneto- and electroencephalography. This term typically refers to a behavioral state characterized by the absence of any task or stimuli. The corresponding neuronal activity is often called idle or ongoing. Numerous modeling studies on spiking neural networks claim to mimic such idle states, but compare their results with  ...[more]

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