Predicting upcoming actions by observation: some facts, models and challenges

C. D. Vargas, M. L. Rangel, A. Galves

Predicting another person's upcoming action to build an appropriate response is a regular occurrence in the domain of motor control. In this review we discuss conceptual and experimental approaches aiming at the neural basis of predicting and learning to predict upcoming movements by their observation.

LimeSurvey, a key tool for open-science data building

The NeuroMat technology-transfer team has been active in the creation and spreading of free and open-source software tools and materials on how to use efficiently these tools. Such activities are in tune with the commitment that NeuroMat, the Research, Innovation and Dissemination Center for Neuromathematics, has embraced in relying exclusively on open-science tools and becoming a reference in open science.

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.


On the Number of Orientations of Random Graphs with No Directed Cycles of a Given Length

P. Allen, Y. Kohayakawa, G. O. Mota, R. F. Parente

Let H⃗ be an orientation of a graph H. Alon and Yuster proposed the problem of determining or estimating D(n,m,H⃗), the maximum number of H⃗-free orientations a graph with n vertices and m edges may have. We consider the maximum number of H⃗-free orientations of typical graphs G(n,m) with n vertices and m edges. Suppose H⃗ =C↻ℓ is the directed cycle of length ℓ≥3. We show that if m≫n^(1+1/(ℓ−1)), then this maximum is 2^o(m), while if m≪n^(1+1/(ℓ−1)), then it is 2^(1−o(1))m.

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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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