Special Issue "Stochastic Processes in Neuronal Modeling"

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: 30 November 2019

Special Issue Editors

Guest Editor
Prof. Enrica Pirozzi

Department of Mathematics and Applications "R.Caccioppoli", University of Naples Federico II, Italy
Website | E-Mail
Interests: markov processes; semi-markov processes; first passage problems; fractional dynamics; fractional brownian motion; coupled dynamics; queuing theory; leaky integrate-and-fire neuronal models; computational methods for stochastic models; stochastic simulation techniques
Guest Editor
Prof. Eva Löcherbach

Université Paris 1 Panthéon Sorbonne, 90 rue de Tolbiac, 75013 Paris, France
Website | E-Mail
Interests: interacting particle systems; stochastic models in neuroscience; longtime behavior of stochastic processes; coupling and perfect simulation; Hawkes processes; chains and processes with memory of variable length

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to publish original research articles covering advances in the theory of stochastic processes and stochastic models for single and networks of neurons. In particular, with the aim of improving upon existing neuronal models or creating new models, continuous and discrete time stochastic processes will be discussed, as well as stochastic differential equations, fractional differential equations, correlated processes, first passage time problems, stochastic optimal controls, mean-field limits, interacting particle systems, statistics of stochastic processes, parameter estimation and simulation techniques.

Potential topics include, but are not limited to:

-Markov and semi-Markov processes

-Time-changed processes

-Markov chains

-Jump processes

-Coupled dynamics

-Fractional processes

-Space-time fractional equations

-Fractional Brownian motion

-Long-range dependence

-Hawkes processes

-Integrate and fire models

-Mean-field limits

-Stochastic optimal control

-Population models

-Computational methods for stochastic models

-Information theory and estimation theory for computational neuroscience

-Large deviations and limit theorems

-Numerical and simulations approaches

Prof. Enrica Pirozzi
Prof. Eva Löcherbach
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Stochastic differential equations
  • Neuronal models
  • Fractional dynamics
  • Long-range dependence
  • Population models
  • Information measures
  • Statistics
  • Simulation

Published Papers (1 paper)

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Research

Open AccessArticle
Retrieving a Context Tree from EEG Data
Mathematics 2019, 7(5), 427; https://doi.org/10.3390/math7050427
Received: 28 March 2019 / Revised: 30 April 2019 / Accepted: 5 May 2019 / Published: 14 May 2019
PDF Full-text (374 KB) | HTML Full-text | XML Full-text
Abstract
It has been repeatedly conjectured that the brain retrieves statistical regularities from stimuli. Here, we present a new statistical approach allowing to address this conjecture. This approach is based on a new class of stochastic processes, namely, sequences of random objects driven by [...] Read more.
It has been repeatedly conjectured that the brain retrieves statistical regularities from stimuli. Here, we present a new statistical approach allowing to address this conjecture. This approach is based on a new class of stochastic processes, namely, sequences of random objects driven by chains with memory of variable length. Full article
(This article belongs to the Special Issue Stochastic Processes in Neuronal Modeling)
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