Borges, F. S.
Protachevicz, P. R.
Pena, R. F. O.
Lameu, E. L.
Higa, V, G. S.
Kihara, J. A. H.
Matias, F. S.
Antonopoulos, C. G.
de Pasquale, R.
Roque, A. C.
Iarosz, K. C.
Batista, A. M.
 Fed Univ ABC, Ctr Math Computat & Cognit, Sao Bernardo Do Campo, SP - Brazil
 Univ Estadual Ponta Grossa, Grad Sci Program Phys, Ponta Grossa, PR - Brazil
 Univ Sao Paulo, Dept Phys, Lab Neural Syst, Ribeirao Preto, SP - Brazil
 Humboldt Univ, Dept Phys, Berlin - Germany
 Natl Inst Space Res, Sao Jose Dos Campos, SP - Brazil
 Higa, G. S., V, Fed Univ ABC, Ctr Math Computat & Cognit, Sao Bernardo Do Campo, SP - Brazil
 Univ Fed Alagoas, Inst Phys, Maceio, Alagoas - Brazil
 Univ Paris Saclay, Univ Paris Sud, Cognit Neuroimaging Unit, CEA DRF I2BM, INSERM, F-91191 Gif Sur Yvette - France
 Univ Essex, Dept Math Sci, Wivenhoe Pk, Colchester, Essex - England
 Univ Sao Paulo, Dept Physiol & Biophys, ICB, Sao Paulo, SP - Brazil
 Univ Sao Paulo, Inst Phys, Sao Paulo, SP - Brazil
 Fudan Univ, Minist Educ, Key Lab Computat Neurosci & Brain Inspired Intell, Shanghai - Peoples R China
 Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai - Peoples R China
 Univ Estadual Ponta Grossa, Dept Math & Stat, Ponta Grossa, PR - Brazil
Número total de Afiliações: 14
Tipo de documento:
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS;
JAN 1 2020.
Citações Web of Science:
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. (C) 2019 Elsevier B.V. All rights reserved. (AU)