*Antonio Galves, Eva Löcherbach, Christophe Pouzat, Errico Presutti*

In this paper we present a simple microscopic stochastic model describing short term plasticity within a large homogeneous network of interacting neurons. Each neuron is represented by its membrane potential and by the residual calcium concentration within the cell at a given time. Neurons spike at a rate depending on their membrane potential. When spiking, the residual calcium concentration of the spiking neuron increases by one unit. Moreover, an additional amount of potential is given to all other neurons in the system. This amount depends linearly on the current residual calcium concentration within the cell of the spiking neuron. In between successive spikes, the potentials and the residual calcium concentrations of each neuron decrease at a constant rate.

*Morgan André*

In 2018, Ferrari et al. wrote a paper called “Phase Transition for Infinite Systems of Spiking Neurons” in which they introduced a continuous time stochastic model of interacting neurons. This model consists in a countable number of neurons, each of them having an integer-valued membrane potential, which value determine the rate at which the neuron spikes. This model has also a parameter 𝛾, corresponding to the rate of the leak times of the neurons, that is, the times at which the membrane potential of a given neuron is spontaneously reset to its resting value (which is 0 by convention). As its title says, it was proven in this previous article that this model presents a phase transition phenomenon with respect to 𝛾. Here we prove that this model also exhibits a metastable behavior. By this we mean that if 𝛾 is small enough, then the re-normalized time of extinction of a finite version of this system converges toward an exponential random variable of mean 1 as the number of neurons goes to infinity.

This non-exhaustive list brings up some of the key achievements of the Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat/FAPESP) in 2019. It is not conceived as a Statement of Impact --as the 2018 SOI--, since it may not be seen as an official *compte-rendu*; it is rather an informal recompilation of key moments of our team. This accounts for activities and milestones since NeuroMat's renewal for six years.

The Research, Innovation and Dissemination Center for Neuromathematics (NeuroMat) will hold the “Second NeuroMat Young Researchers Workshop” in São Paulo on November 27. NeuroMat is hosted by the University of São Paulo and funded by the São Paulo Research Foundation (FAPESP). The event's official website is: neuromat.numec.prp.usp.br/2young. The aim of this meeting is to get members of the NeuroMat team to know more about each other's research. To do so, every Master/PhD student and Postdoc is invited to give a small presentation (around 25 minutes) about their current work. The meeting will take place at the Multipurpose Auditorium at NeuroMat, Av. Prof. Luciano Gualberto, 1171, at the University of São Paulo, São Paulo, Brazil.

*Vinícius Lima Cordeiro, Rodrigo Felipe de Oliveira Pena, Cesar Augusto Celis Ceballos, Renan Oliveira Shimoura and Antonio Carlos Roque*

Neurons respond to external stimuli by emitting sequences of action potentials (spike trains). In this way, one can say that the spike train is the neuronal response to an input stimulus. Action potentials are “all-or-none” phenomena, which means that a spike train can be represented by a sequence of zeros and ones. In the context of information theory, one can then ask: how much information about a given stimulus the spike train conveys? Or rather, what aspects of the stimulus are encoded by the neuronal response? In this article, an introduction to information theory is presented which consists of historical aspects, fundamental concepts of the theory, and applications to neuroscience. The connection to neuroscience is made with the use of demonstrations and discussions of different methods of the theory of information. Examples are given through computer simulations of two neuron models, the Poisson neuron and the integrate-and-fire neuron, and a cellular automata network model. In the latter case, it is shown how one can use information theory measures to retrieve the connectivity matrix of a network.

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