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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A unified theory of E/I synaptic balance, quasicritical neuronal avalanches and asynchronous irregular spiking

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Girardi-Schappo, Mauricio [1] ; Galera, Emilio F. [2] ; Carvalho, Tawan T. A. [3] ; Brochini, Ludmila [4] ; Kamiji, Nilton L. [2] ; Roque, Antonio C. [2] ; Kinouchi, Osame [2]
Total Authors: 7
[1] Univ Ottawa, Dept Phys, Ottawa, ON K1N 6N5 - Canada
[2] Univ Sao Paulo, FFCLRP, Dept Fis, BR-14040901 Ribeirao Preto, SP - Brazil
[3] Univ Fed Pernambuco, Dept Fis, BR-50670901 Recife, PE - Brazil
[4] Univ Sao Paulo, Inst Matemat & Estat, BR-05508090 Sao Paulo, SP - Brazil
Total Affiliations: 4
Document type: Journal article
Web of Science Citations: 0

Neuronal avalanches and asynchronous irregular (AI) firing patterns have been thought to represent distinct frameworks to understand the brain spontaneous activity. The former is typically present in systems where there is a balance between the slow accumulation of tension and its fast dissipation, whereas the latter is accompanied by the balance between synaptic excitation and inhibition (E/I). Here, we develop a new theory of E/I balance that relies on two homeostatic adaptation mechanisms: the short-term depression of inhibition and the spike-dependent threshold increase. First, we turn off the adaptation and show that the so-called static system has a typical critical point commonly attributed to self-organized critical models. Then, we turn on the adaptation and show that the network evolves to a dynamic regime in which: (I) E/I synapses balance for large recovery time scales; (II) an AI firing pattern emerges; and (III) neuronal avalanches display power laws. This is the first time that these three phenomena appear simultaneously in the same network activity. Thus, we show that AI activity and PL avalanches may coexist into a single dynamics, provided that adaptation mechanisms are in place. In our model, the AI firing pattern is a direct consequence of the hovering close to the critical line where external inputs are compensated by threshold growth, creating synaptic balance for any E/I weight ratio. (AU)

FAPESP's process: 19/12746-3 - Phase transitions in neuroscience
Grantee:Osame Kinouchi Filho
Support Opportunities: Scholarships abroad - Research
FAPESP's process: 18/09150-9 - Stochastic and/or computational modeling of the brain functioning
Grantee:Mauricio Girardi Schappo
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 18/20277-0 - Computational and systems neuroscience
Grantee:Antonio Carlos Roque da Silva Filho
Support Opportunities: Regular Research Grants
FAPESP's process: 16/24676-1 - Context trees applied to the statistical modeling of neural spike trains
Grantee:Ludmila Brochini Rodrigues
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat
Grantee:Oswaldo Baffa Filho
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 16/03855-5 - Analysis on statistical selection of visual cortex and retina models: a comparative study
Grantee:Nilton Liuji Kamiji
Support Opportunities: Scholarships in Brazil - Post-Doctoral