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- W3006704054 abstract "Abstract Behaving animals have to process partial or incomplete information to plan movements. Neural activity and behavior of highly trained animals are strikingly variable across behavioral trials. In the present study, we show that incomplete information is reflected in both neural and behavioral variability. We study a mechanism for such variability in spiking neural network models with cluster topologies that enables multistability and attractor dynamics. Multistable attractors have been studied in spiking neural networks through clusters of strongly interconnected excitatory neurons. However, we show that existing models suffer from weak robustness and fail to reproduce the experimental findings. To overcome these problems, we introduce a novel architecture with excitatory and inhibitory clusters of spiking neurons. In contrast to earlier prominent models of excitatory clusters, this model ensures the local balance of excitation and inhibition and shows robust multistability across a wide range of network parameters. Furthermore, with the appropriate stimulation of network clusters, this network topology enables qualitatively and quantitatively reproducing in vivo firing rates, variability dynamics and behavioral reaction times for different task conditions as observed in recordings from the motor cortex of behaving monkeys." @default.
- W3006704054 created "2020-03-06" @default.
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- W3006704054 date "2020-02-28" @default.
- W3006704054 modified "2023-10-17" @default.
- W3006704054 title "Excitatory and inhibitory motor cortical clusters account for balance, variability, and task performance" @default.
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- W3006704054 doi "https://doi.org/10.1101/2020.02.27.968339" @default.
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