Today RIDC NeuroMat will hold another one of its seminars. This time, the NeuroMat distinguished associate investigators Eva Löcherbach will present "A system of interacting neurons with short term plasticity".
The project that created the Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) in 2013 has been renewed by the São Paulo Research Foundation for a six-year term, and in this context we launch a series to connect our past and current trajectory. This series has been called "First 5, Next 6", a direct reference to content produced by our team within the center renewal process, especially a presentation to FAPESP's International Assessment Committee in 2017. The first part of this text is informed by NeuroMat's Statement of Impact, again a document prepared in the renewal process.
This month on "Jornal da USP" website, there was a report on the relevance of comics in the context of scientific diffusion. In this context, "Os Braços de Nildo e Rony" is a comic book by Antonio Galves, professor of the Institute of Mathematics and Statistics (IME-USP) and coordinator of RIDC NeuroMat. The material seeks to guide patients who have suffered a traumatic injury of the brachial plexus, a set of nerves that makes communication between the upper limbs and the brain. The injuries affect many victims of traffic accidents, mainly involving motorcycles. Jornal da USP, 20/02/2019. (In Portuguese.)
Ludmila Brochini, Ariadne de Andrade Costa, Miguel Abadi, Antônio C. Roque, Jorge Stolfi and Osame Kinouchi
Phase transitions and critical behavior are crucial issues both in theoretical and experimental neuroscience. We report analytic and computational results about phase transitions and self-organized criticality (SOC) in networks with general stochastic neurons. The stochastic neuron has a firing probability given by a smooth monotonic function Φ(V) of the membrane potential V, rather than a sharp firing threshold. We find that such networks can operate in several dynamic regimes (phases) depending on the average synaptic weight and the shape of the firing function Φ. In particular, we encounter both continuous and discontinuous phase transitions to absorbing states. At the continuous transition critical boundary, neuronal avalanches occur whose distributions of size and duration are given by power laws, as observed in biological neural networks. We also propose and test a new mechanism to produce SOC: the use of dynamic neuronal gains – a form of short-term plasticity probably located at the axon initial segment (AIS) – instead of depressing synapses at the dendrites (as previously studied in the literature). The new self-organization mechanism produces a slightly supercritical state, that we called SOSC, in accord to some intuitions of Alan Turing.
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. All codes used in the simulations were made publicly available at the GitHub platform and are accessible through the URL: github.com/ViniciusLima94/ticodigoneural.
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