@Article{BorgesLIPCVMBB:2018:InToNa,
author = "Borges, F. S. and Lameu, Ewandson Luiz and Iarosz, K. C. and
Protachevicz, P. R. and Caldas, I. L. and Viana, R. L. and Macau,
Elbert Einstein Nehrer and Batista, A. M. and Baptista, M. S.",
affiliation = "{Universidade de S{\~a}o Paulo (USP)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Universidade de S{\~a}o Paulo
(USP)} and {Universidade Estadual de Ponta Grossa (UEPG)} and
{Universidade de S{\~a}o Paulo (USP)} and {Universidade Federal
do Paran{\'a} (UFPR0} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Universidade de S{\~a}o Paulo (USP)} and
{University of Aberdeen}",
title = "Inference of topology and the nature of synapses, and the flow of
information in neuronal networks",
journal = "Physical Review E",
year = "2018",
volume = "97",
number = "2",
pages = "e022303",
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.",
doi = "10.1103/PhysRevE.97.022303",
url = "http://dx.doi.org/10.1103/PhysRevE.97.022303",
issn = "1539-3755",
label = "self-archiving-INPE-MCTIC-GOV-BR",
language = "en",
targetfile = "borges_inference.pdf",
urlaccessdate = "28 abr. 2024"
}