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Mathematical Biosciences and Engineering

2019, Volume 16, Issue 6: 8214-8216. doi: 10.3934/mbe.2019415
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Preface

Special Issue: Neural Coding 2018

  • 1.
    Institute of Physiology CAS, Czech Republic
  • 2.
    Department of Mathematics, University of Torino, Italy
  • 3.
    Institute for Stochastics, Johannes Kepler University Linz, Austria
  • Received: 10 September 2019 Accepted: 10 September 2019 Published: 12 September 2019

  • The special issue is available from: https://www.aimspress.com/newsinfo/1269.html.

    Citation: Lubomir Kostal, Laura Sacerdote, Massimiliano Tamborrino. Special Issue: Neural Coding 2018[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 8214-8216. doi: 10.3934/mbe.2019415

    Related Papers:

  • The special issue is available from: https://www.aimspress.com/newsinfo/1269.html.


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    [1] G. Ascione and E. Pirozzi, On a stochastic neuronal model integrating correlated inputs, Math. Biosci. Eng., 16 (2019), 5206–5225.
    [2] A. Di Crescenzo and F. Travaglino, Probabilistic analysis of systems alternating for state-dependent dichotomous noise, Math. Biosci. Eng., 16 (2019), 6386–6405.
    [3] P. E. Greenwood and L. M. Ward, Rapidly forming, slowly evolving, spatial patterns from quasicycle Mexican Hat coupling, Math. Biosci. Eng., 16 (2019), 6769–6793.
    [4] J. Ito, E. Lucrezia, G. Palm and S. Grün, Detection and evaluation of bursts in terms of novelty and surprise, Math. Biosci. Eng., 16 (2019), 6990–7008.
    [5] D. Fasoli and S. Panzeri, Mathematical studies of the dynamics of finite-size binary neural networks: A review of recent progress, Math. Biosci. Eng., 16 (2019), 8025–8059.
    [6] A. Civallero and C. Zucca, The Inverse First Passage time method for a two dimensional Ornstein Uhlenbeck process with neuronal application, Math. Biosci. Eng., 16 (2019), 8162–8178.
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  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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