Inhibitory loop robustly induces anticipated synchronization in neuronal microcircuits

Fernanda S. Matias, Leonardo L. Gollo, Pedro V. Carelli, Claudio R. Mirasso and Mauro Copelli

We investigate the synchronization properties between two excitatory coupled neurons in the presence of an inhibitory loop mediated by an interneuron. Dynamic inhibition together with noise independently applied to each neuron provide phase diversity in the dynamics of the neuronal motif. We show that the interplay between the coupling strengths and the external noise controls the phase relations between the neurons in a counterintuitive way. For a master-slave configuration (unidirectional coupling) we find that the slave can anticipate the master, on average, if the slave is subject to the inhibitory feedback. In this nonusual regime, called anticipated synchronization (AS), the phase of the postsynaptic neuron is advanced with respect to that of the presynaptic neuron. We also show that the AS regime survives even in the presence of unbalanced bidirectional excitatory coupling. Moreover, for the symmetric mutually coupled situation, the neuron that is subject to the inhibitory loop leads in phase.

Vagas para pós-doutorado

O Centro de Pesquisa, Inovação e Difusão em Neuromatemática (NeuroMat), com sede na Universidade de São Paulo, Brasil, e financiado pela FAPESP, está selecionando candidatos a bolsas de pós-doutoramento para desenvolvimento de atividades de pesquisa na área de interface entre Matemática, Estatística, Computação e Neurociência.

Wikipedia is an effective tool for education

During her visit at NeuroMat, LiAnna Davis, director of the Wiki Edu Foundation, talked on how Wikimedia projects may have an impact in fostering scientific dissemination. Thais Paiva, Carta Capital (Carta Educação), 3/15/2017. (In Portuguese)

Potential Well Spectrum and Hitting Time in Renewal Processes

Miguel Abadi, Liliam Cardeño and Sandro Gallo

The potential well of a state can be interpreted physically as the energy that a stationary process needs to leave the state. We prove that for discrete time renewal processes, the potential well is the right scaling for the hitting and return time distributions of the state. We further detail the potential well spectrum of these processes by giving a complete classification of the states according to their potential well.

Estimating Parameters Associated with Monotone Properties

Carlos Hoppen, Yoshiharu Kohayakawa, Richard Lang, Hanno Lefmann and Henrique Stagni

There has been substantial interest in estimating the value of a graph parameter, i.e., of a real function defined on the set of finite graphs, by sampling a randomly chosen substructure whose size is independent of the size of the input. Graph parameters that may be successfully estimated in this way are said to be testable or estimable, and the sample complexity qz = qz(ε) of an estimable parameter z is the size of the random sample required to ensure that the value of z(G) may be estimated within error ε with probability at least 2/3. In this paper, we study the sample complexity of estimating two graph parameters associated with a monotone graph property, improving previously known results. To obtain our results, we prove that the vertex set of any graph that satisfies a monotone property P may be partitioned equitably into a constant number of classes in such a way that the cluster graph induced by the partition is not far from satisfying a natural weighted graph generalization of P. Properties for which this holds are said to be recoverable, and the study of recoverable properties may be of independent interest

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O Centro de Pesquisa, Inovação e Difusão em Neuromatemática está sediado na Universidade de São Paulo e é financiado pela FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo).

 

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