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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.)

Inference of topology and the nature of synapses, and the flow of information in neuronal networks

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Author(s):
Borges, F. S. [1, 2] ; Lameu, E. L. [3] ; Iarosz, K. C. [1, 4] ; Protachevicz, P. R. [5] ; Caldas, I. L. [1] ; Viana, R. L. [6] ; Macau, E. E. N. [7] ; Batista, A. M. [1, 4, 8] ; Baptista, M. S. [4]
Total Authors: 9
Affiliation:
[1] Univ Sao Paulo, Phys Inst, BR-05508090 Sao Paulo, SP - Brazil
[2] Fed Univ ABC, Ctr Math Computat & Cognit, BR-09606045 Sao Bernardo Do Campo, SP - Brazil
[3] Natl Inst Space Res, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[4] Univ Aberdeen, SUPA, Inst Complex Syst & Math Biol, Aberdeen AB24 3FX - Scotland
[5] Univ Estadual Ponta Grossa, Postgrad Sci, BR-84030900 Ponta Grossa, PR - Brazil
[6] Univ Fed Parana, Phys Dept, BR-81531980 Curitiba, Parana - Brazil
[7] Univ Fed Sao Paulo, BR-12231280 Sao Jose Dos Campos, SP - Brazil
[8] Univ Estadual Ponta Grossa, Math & Stat Dept, BR-84030900 Ponta Grossa, PR - Brazil
Total Affiliations: 8
Document type: Journal article
Source: Physical Review E; v. 97, n. 2 FEB 7 2018.
Web of Science Citations: 1
Abstract

The characterization of neuronal connectivity is one of the most important matters in neuroscience. In this work, we show that a recently proposed informational quantity, the causal mutual information, employed with an appropriate methodology, can be used not only to correctly infer the direction of the underlying physical synapses, but also to identify their excitatory or inhibitory nature, considering easy to handle and measure bivariate time series. The success of our approach relies on a surprising property found in neuronal networks by which nonadjacent neurons do ``understand{''} each other (positive mutual information), however, this exchange of information is not capable of causing effect (zero transfer entropy). Remarkably, inhibitory connections, responsible for enhancing synchronization, transfer more information than excitatory connections, known to enhance entropy in the network. We also demonstrate that our methodology can be used to correctly infer directionality of synapses even in the presence of dynamic and observational Gaussian noise, and is also successful in providing the effective directionality of intermodular connectivity, when only mean fields can be measured. (AU)

FAPESP's process: 15/07311-7 - Dynamic behaviour of neural networks
Grantee:Kelly Cristiane Iarosz
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 11/19296-1 - Nonlinear dynamics
Grantee:Iberê Luiz Caldas
Support type: Research Projects - Thematic Grants
FAPESP's process: 16/23398-8 - Synaptic plasticity in neuronal networks with time delay
Grantee:Ewandson Luiz Lameu
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 16/16148-5 - Synchronous behaviour and synaptic plasticity in complex networks
Grantee:Kelly Cristiane Iarosz
Support type: Scholarships abroad - Research Internship - Post-doctor
FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support type: Research Projects - Thematic Grants