Self-sustained activity of low firing rate in balanced networks

F.S. Borges, P.R. Protachevicz, R.F.O. Pena, E.L. Lameu, G.S.V. Higa, A.H. Kihara, F.S. Matias, C.G. Antonopoulos, R. de Pasquale, A.C. Roque, K.C. Iarosz, P. Ji and A.M. Batista

Physica A: Statistical Mechanics and its Applications, 2020, vol. 537, issue C

Abstract: Self-sustained activity in the brain is observed in the absence of external stimuli and contributes to signal propagation, neural coding, and dynamic stability. It also plays an important role in cognitive processes. In this work, by means of studying intracellular recordings from CA1 neurons in rats and results from numerical simulations, we demonstrate that self-sustained activity presents high variability of patterns, such as low neural firing rates and activity in the form of small-bursts in distinct neurons. In our numerical simulations, we consider random networks composed of coupled, adaptive exponential integrate-and-fire neurons. The neural dynamics in the random networks simulates regular spiking (excitatory) and fast spiking (inhibitory) neurons. We show that both the connection probability and network size are fundamental properties that give rise to self-sustained activity in qualitative agreement with our experimental results. Finally, we provide a more detailed description of self-sustained activity in terms of lifetime distributions, synaptic conductances, and synaptic currents.

Keywords: Spontaneous activity; Neural networks; Whole-cell recordings; Asynchronous irregular activity (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315249

DOI: 10.1016/j.physa.2019.122671

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