Lectures

Perspectives on Applications of a Stochastic Spiking Neuron Model to Neural Network Modeling

Perspectives on Applications of a Stochastic Spiking Neuron Model to Neural Network ModelingLecture by Antonio C. Roque.

This talk presents results of analytical and numerical investigations on the use of the Galves-Löcherbach stochastic neuron to model networks of spiking neurons. The work is done in collaboration with NeuroMat researchers Dr. Ludmila Brochini, Dr. Ariadne Costa, Dr. Miguel Abadi, Dr. Jorge Stolfi, Dr. Osame Kinouchi and students Renan Shimoura and Vinícius Cordeiro.Presented.

Presented at Perspectives in Nonlinear Dynamics conference, Berlin, July 26, 2016.

Hidden context tree modeling of EEG data

Hidden context tree modeling of EEG dataLecture by Antonio Galves.

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.

Presented at MathStatNeuro Workshop, Laboratoire J. A. Dieudonné, Nice, France, September 10, 2015.

Goodness–of–fit tests for regression models: the functional data case

Goodness–of–fit tests for regression models: the functional data caseIn this talk the topic of the goodness–of–fit for regression models with functional covariates is considered. Although several papers have been published in the last two decades for the checking of regression models, the case where the covariates are functional is quite recent and has became of interest in the last years. We will review the very recent advances in this area and we will propose a new goodness–of–fit test for the null hypothesis of a functional linear model with scalar response. Lecturer: Wenceslao González-Manteiga, Univ. de Santiago de Compostela, Spain.

Functional Regression Analysis

Functional Regression AnalysisThe aim of this presentation is to revise the functional regression models with scalar response (Linear, Nonlinear and Semilinear) and the extension to the more general case where the response belongs to the exponential family (binomial, poisson, gamma, ...). This extension allows to develop new functional classification methods based on this regression models. Some examples along with code implementation in R are provided during the talk. Lecturer: Manuel Febrero Bande, Univ. de Santiago de Compostela, Spain.

An introduction to the storage of experimental data in neuroscience

Introdução ao Armazenamento de Dados de Experimentos em Neurociência - Parte 01A set of presentations and background material on strategies to store experimental neuroscientific data, digital questionnaires to collect and store experimental data and meta-data and tools to manage files. Lecturers: Profs. Kelly Braghetto (DCC-IME-USP) and Amanda Nascimento (DC-UFOP).

Spike sorting: What is it? Why do we need it? Where does it come from? How is it done? How to interpret it?

Spike sorting: What is it? Why do we need it? Where does it come from? How is it done? How to interpret it?A series of lectures on spike sorting. Lecturer: Prof. Christophe Pouzat, a CNRS researcher of the Applied Maths Laboratory of the Paris-Descartes University and a specialist in spike sorting.


Mathematical and computational challenges of neuroscience

Desafios matemáticos e computacionais da neurociênciaDoes the brain of a child with ADHD process information in the same way of the brain of a child with no such disorder? To develop mathematical models to make sense of meaningful differences is a key challenge of Neuromathematics. And this becomes a computational challenge, to the extent it requires the building of neuroscientific databases that take into consideration all characteristics of patients and experiments. Lecturers: Michelle Miranda and Evandro Santos Rocha, NeuroMat team members.

An elementary introduction to the stochastic modelling of symbolic chains

Introdução elementar à modelagem estocástica de cadeias simbólicasA class on statistical regularities and statistical model selection. Lecturer: Prof. Antonio Galves, NeuroMat principal investigator and professor at the University of São Paulo's Institute of Mathematics and Statistics.


Data Provenance and Scientific Workflow Management

Data Provenance and Scientific Workflow Management Introductory class on techniques and tools to manage scientific data, focusing on sources of information and data analysis. Lecturer: Prof. Kelly Rosa Braghetto, a NeuroMat associate investigator and a professor at the University of São Paulo's Department of Computer Science.


 

NeuroMat

The Research, Innovation and Dissemination Center for Neuromathematics is hosted by the University of São Paulo and funded by FAPESP (São Paulo Research Foundation).

 

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