NeuroMat to organize a roundtable on challenges of scientific dissemination in praise of physician Ernesto Hamburger

The roundtable "Challenges of Scientific Dissemination: in praise of Ernesto Hamburger" will be an opportunity to discuss the role of scientific communication, exhibition and education in Brazil, on June, 9, at 6:00PM, at the Institute of Physics, at the University of São Paulo. The organizer of the event is the dissemination team of the Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat).

Opening for photo or audiovisual scientific journalism scholarship

The Research, Innovation and Dissemination Center on Neuromathematics (RIDC NeuroMat) is offering a scholarship for young professionals of scientific journalism interested in being part of a breakthrough and innovative scientific project. (In Portuguese).

NeuroMat investigates how much time a brain takes to make a decision

On May 15, the member of the Research, Innovation and Dissemination Center for Neuromathematics (NeuroMat) André Frazão Helene and colleagues published "On Sequence Learning Models: Open-loop Control Not Strictly Guided by Hick’s Law", on Scientific Reports, a journal of Nature's group. "The meaning is that the nervous system, relying on past experiences, generates predictions on what will happen in the future," the researcher explained. Report by Tabita Said, Jornal da USP, 5/23/2016. (In Portuguese).

Opening for Architecture and Software Development Scholarship

The Research, Innovation and Dissemination Center on Neuromathematics (NeuroMat) is offering a scholarship for information technology professionals interested in being part of a breakthrough and innovative scientific project.

Non-parametric estimation of the spiking rate in systems of interacting neurons

Pierre Hodara, Nathalie Krell, Eva Löcherbach

We consider a model of interacting neurons where the membrane potentials of the neurons are described by a multidimensional piecewise deterministic Markov process (PDMP) with values in ℝN, where N is the number of neurons in the network. A deterministic drift attracts each neuron's membrane potential to an equilibrium potential m. When a neuron jumps, its membrane potential is reset to 0, while the other neurons receive an additional amount of potential 1/N. We are interested in the estimation of the jump (or spiking) rate of a single neuron based on an observation of the membrane potentials of the N neurons up to time t. We study a Nadaraya-Watson type kernel estimator for the jump rate and establish its rate of convergence in Lˆ2. This rate of convergence is shown to be optimal for a given H\"older class of jump rate functions. We also obtain a central limit theorem for the error of estimation. The main probabilistic tools are the uniform ergodicity of the process and a fine study of the invariant measure of a single neuron.

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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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