The Fellows Programme is part of the Frictionless Data for Reproducible Research project at Open Knowledge Foundation, a global, non-profit network that promotes and shares information at no charge, including both content and data. This project, funded by the Sloan Foundation, applies to work in Frictionless Data to data-driven research disciplines, in order to facilitate data workflows in research contexts. During the first half of 2019, Neuroscience Experiments System (NES) was selected to be a funded project of Frictionless Data. As you may know, NES is an open-source tool being developed that aims to assist neuroscience research laboratories in routine procedures for data collection. NES was developed to store a large amount of data in a structured way, allowing researchers to seek and share data and metadata of neuroscience experiments. To the best of our knowledge, there are no open-source software tools which provide a way to record data and metadata involved in all steps of an electrophysiological experiment and also register experimental data and its fundamental provenance information. With the anonymization of sensitive information, the data collected using NES can be publicly available through the NeuroMat Open Database, which allows any researcher to reproduce the experiment or simply use the data in a different study.
The Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) will launch in August the podcast "A Matemática do Cérebro" --in Portuguese, Mathematics of the Brain. This resource will be available on the most important podcast technologies and also hosted on its own website. The production of the podcast is led by NeuroMat director Antonio Galves and the newest member of the RIDC, Eduardo Vicente, from the University of São Paulo School of Communications and Arts.
Zacharias L. R., Peres A. S. C., Souza V. H., Conforto A. B. and Baffa O.
Small variations in TMS parameters, such as pulse frequency and amplitude may elicit distinct neurophysiological responses. Assessing the mismatch between nominal and experimental parameters of TMS stimulators is essential for safe application and comparisons of results across studies.
Pierre Hodara and Ioannis Papageorgiou
We aim to prove Poincaré inequalities for a class of pure jump Markov processes inspired by the model introduced by Galves and Löcherbach to describe the behavior of interacting brain neurons. In particular, we consider neurons with degenerate jumps, i.e., which lose their memory when they spike, while the probability of a spike depends on the actual position and thus the past of the whole neural system. The process studied by Galves and Löcherbach is a point process counting the spike events of the system and is therefore non-Markovian. In this work, we consider a process describing the membrane potential of each neuron that contains the relevant information of the past. This allows us to work in a Markovian framework.
A recent NeuroMat paper has addressed the conjecture that the brain identifies structures from sequences of stimuli. It means that in order to make predictions the brain analyzes structured sequences of stimuli and retrieves from them statistical regularities. This classical conjecture is often called "Statistician Brain Conjecture" and is associated to studies on how one learns. The NeuroMat research team has introduced a new class of stochastic processes --sequences of random objects driven by chains with memory of variable length-- to address this conjecture.
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