NeuroMat research-team members have been involved in quantifying semantics as a means of understanding behavior and in a recent publication have tested automated speech analyses combined with machine learning processes to predict psychosis onset in youths at clinical high-risk for psychosis. NeuroMat members Guillermo Cecchi, from the IBM T. J. Watson Research Center, Sidarta Ribeiro, from the Federal University of Rio Grande do Norte, Mauro Copelli, from the Federal University of Pernambuco, and colleagues have shown that speech features predicted later psychosis development with 100% accuracy. NeuroMat is the São Paulo Research Foundation (FAPESP)’s Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) and was launched in 2013 to contribute to creating a mathematical framework for neuroscience.
Luc Devroye, Matthieu Lerasle, Gabor Lugosi, Roberto I. Oliveira
The Research, Innovation and Dissemination Center for Neuromathematics (NeuroMat)'s development team has released two tutorials pertaining to the ongoing project of an open-source tool to manage clinical data gathered in hospitals and research institutions, the Neuroscience Experiments System (NES). Tutorial 1 brings up information on how to create a survey using LimeSurvey, an online tool that is consistent to NES, whereas Tutorial 2 shows how to integrate a LimeSurvey document onto NES. (In Portuguese; English version to be released.)
News
| Sep 22, 2015
The Research, Innovation and Dissemination Center for Neuromathematics (NeuroMat), one of the Research, Innovation and Dissemination Centers (RIDCs) funded by FAPESP, has started the development of a "supercomputer" that simulates the functioning of the cerebral cortex, a key area of the central nervous system. Report by Diego Freire, Agência Fapesp, 09/22/2015. (In Portuguese).
In this talk a new class of stochastic processes is presented: the Hidden Context Tree Models (HCTM). This class gives a natural framework to mathematically address the neurobiological conjecture about the ability of the brain to identify the structure of a random source. Statistical model selection in a suitable subclass of HCTMs can provide experimental evidence supporting this conjecture. A case study with EEG data will be also presented. This is a joint work with A. Duarte, R. Fraiman, G. Ost and C. Vargas.
