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Mathematical Biosciences and Engineering

2016, Volume 13, Issue 3: 597-611. doi: 10.3934/mbe.2016010
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A new firing paradigm for integrate and fire stochastic neuronal models

  • 1.
    Department of Mathematics "G. Peano", University of Torino, Via Carlo Alberto 10, 10123 Torino
  • 2.
    Department of Mathematics G. Peano, University of Torino, Via Carlo Alberto 10, 10123 - Torino
  • Received: 01 May 2015 Accepted: 29 June 2018 Published: 01 January 2016

  • MSC : 60J99, 92Bxx.

  • A new definition of firing time is given in the framework of Integrate and Fire neuronal models. The classical absorption condition at the threshold is relaxed and the firing time is defined as the first time the membrane potential process lies above a fixed depolarisation level for a sufficiently long time. The mathematical properties of the new firing time are investigated both for the Perfect Integrator and the Leaky Integrator. In the latter case, a simulation study is presented to complete the analysis where analytical results are not yet achieved.

    Citation: Roberta Sirovich, Luisa Testa. A new firing paradigm for integrate and fire stochastic neuronal models[J]. Mathematical Biosciences and Engineering, 2016, 13(3): 597-611. doi: 10.3934/mbe.2016010

    Related Papers:

  • A new definition of firing time is given in the framework of Integrate and Fire neuronal models. The classical absorption condition at the threshold is relaxed and the firing time is defined as the first time the membrane potential process lies above a fixed depolarisation level for a sufficiently long time. The mathematical properties of the new firing time are investigated both for the Perfect Integrator and the Leaky Integrator. In the latter case, a simulation study is presented to complete the analysis where analytical results are not yet achieved.


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    [1] Queueing Systems, 10 (1992), 5-87.
    [2] ORSA Journal on Computing, 7 (1995), 36-43.
    [3] Stochastic Models, 21 (2005), 967-980.
    [4] Ann. Appl. Probab., 12 (2002), 1071-1095.
    [5] Scandinavian Journal of Statistics, 40 (2013), 274-293.
    [6] Physical Review E, 78 (2008), 011918.
    [7] Physical Review E, 81 (2010), 031916.
    [8] Mathematical Biosciences and Engineering, 11 (2014), 189-201.
    [9] Advances in Applied Probability, 19 (1987), 784-800.
    [10] Biological Cybernetics, 95 (2006), 1-19.
    [11] Biological Cybernetics, 95 (2006), 97-112.
    [12] Journal of Theoretical Biology, 350 (2014), 81-89.
    [13] Frontiers in Neural Circuits, 8 (2014), p11.
    [14] Advances in Applied Probabability, 29 (1997), 165-184.
    [15] Probabilistic Engineering Mechanics, 23 (2008), 170-179.
    [16] Physical Review. E (3), 71 (2005), 011907, 9pp.
    [17] Physical Review E, 73 (2006), 061910, 9pp.
    [18] Journal of Mathematical Biology, 67 (2013), 453-481.
    [19] Bulletin of Mathematical Biology, 75 (2013), 629-648.
    [20] Proceedings of the Royal Society of Edinburgh: Section A Mathematics, 129 (1999), 57-75.
    [21] Biophysical Journal, 4 (1964), 41-68.
    [22] Cambridge University Press, 2002.
    [23] Annals of Probability, 7 (1979), 244-266.
    [24] Advances in Applied Probability, 21 (1989), 20-36.
    [25] Neural Computation, 23 (2011), 1743-1767.
    [26] Comm. Statist. Simulation Comput., 28 (1999), 1135-1163.
    [27] Frontiers in Computational Neuroscience, 7 (2013), p131.
    [28] Biological Cybernetics, 73 (1995), 209-221.
    [29] in Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability (Univ. California, Berkeley, Calif., 1970/1971), Vol. III: Probability theory, Univ. California Press, Berkeley, Calif., 1972, 225-239.
    [30] in Advances in Neural Information Processing Systems 18 (eds. Y. Weiss, B. Sch\"olkopf and J. Platt), MIT Press, 2006, 595-602.
    [31] Springer-Verlag, 1991.
    [32] Frontiers in Computational Neuroscience, 3 (2009), p9.
    [33] Brain Research, 1536 (2013), 97-106.
    [34] Mathematical Bioscience, 67 (1983), 247-260.
    [35] Biological Cybernetics, 99 (2008), 253-262.
    [36] Neural Computation, 16 (2004), 477-489.
    [37] Journal of Computational Neuroscence, 21 (2006), 211-223.
    [38] Courier Corporation, 1972.
    [39] Physical Review E, 72 (2005), 021911, 21pp.
    [40] Springer-Verlag, 2003.
    [41] Bernoulli, 9 (2003), 1-24.
    [42] Springer-Verlag, Berlin-New York, 1977.
    [43] Biological Cybernetics, 35 (1979), 1-9.
    [44] Physical Review E, 76 (2007), 021919.
    [45] Cambridge University Press, Cambridge, 2000.
    [46] in Stochastic Biomathematical Models, Lecture Notes in Math., 2058, Springer, Heidelberg, 2013, 99-148.
    [47] Mathematical Bioscience, 39 (1978), 53-70.
    [48] Lifetime Data Analysis, 21 (2015), 331-352.
    [49] Physica D: Nonlinear Phenomena, 288 (2014), 45-52.
    [50] Cambridge Studies in Mathematical Biology, 8, Cambridge University Press, Cambridge, 1988.
    [51] Cambridge Studies in Mathematical Biology, 8, Cambridge University Press, Cambridge, 1988.
    [52] The Journal of Neuroscience, 24 (2004), 3060-3069.
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  • © 2016 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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