Integrator or coincidence detector --- what shapes the relation of stimulus synchrony and the operational mode of a neuron?

  • Received: 01 March 2015 Accepted: 29 June 2018 Published: 01 January 2016
  • MSC : 92B25, 92C20, 92-08.

  • The operational mode of a neuron (i.e., whether a neuron is an integrator or a coincidence detector) is in part determined by the degree of synchrony in the firing of its pre-synaptic neural population. More specifically, it is determined by the degree of synchrony that causes the neuron to fire. In this paper, we investigate the relationship between the input and the operational mode. We compare the response-relevant input synchrony, which measures the operational mode and can be determined using a membrane potential slope-based measure [7], with the spike time distance of the spike trains driving the neuron, which measures spike train synchrony and can be determined using the multivariate SPIKE-distance metric [10]. We discover that the relationship between the two measures changes substantially based on the values of the parameters of the input (firing rate and number of spike trains) and the parameters of the post-synaptic neuron (synaptic weight, membrane leak time constant and spike threshold). More importantly, we determine how the parameters interact to shape the synchrony-operational mode relationship. Our results indicate that the amount of depolarisation caused by a highly synchronous volley of input spikes, is the most influential factor in defining the relationship between input synchrony and operational mode. This is defined by the number of input spikes and the membrane potential depolarisation caused per spike, compared to the spike threshold.

    Citation: Achilleas Koutsou, Jacob Kanev, Maria Economidou, Chris Christodoulou. Integrator or coincidence detector --- what shapes the relation of stimulus synchrony and the operational mode of a neuron?[J]. Mathematical Biosciences and Engineering, 2016, 13(3): 521-535. doi: 10.3934/mbe.2016005

    Related Papers:

  • The operational mode of a neuron (i.e., whether a neuron is an integrator or a coincidence detector) is in part determined by the degree of synchrony in the firing of its pre-synaptic neural population. More specifically, it is determined by the degree of synchrony that causes the neuron to fire. In this paper, we investigate the relationship between the input and the operational mode. We compare the response-relevant input synchrony, which measures the operational mode and can be determined using a membrane potential slope-based measure [7], with the spike time distance of the spike trains driving the neuron, which measures spike train synchrony and can be determined using the multivariate SPIKE-distance metric [10]. We discover that the relationship between the two measures changes substantially based on the values of the parameters of the input (firing rate and number of spike trains) and the parameters of the post-synaptic neuron (synaptic weight, membrane leak time constant and spike threshold). More importantly, we determine how the parameters interact to shape the synchrony-operational mode relationship. Our results indicate that the amount of depolarisation caused by a highly synchronous volley of input spikes, is the most influential factor in defining the relationship between input synchrony and operational mode. This is defined by the number of input spikes and the membrane potential depolarisation caused per spike, compared to the spike threshold.


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    [1] Israel Journal of Medical Sciences, 18 (1982), 83-92.
    [2] Journal of Physiology Paris, 90 (1996), 243-247.
    [3] Neural Computation, 9 (1997), 985-1000.
    [4] Verlag von Veit & Comp., Leipzig, 1914.
    [5] Neural Computation, 11 (1999), 1139-1154.
    [6] Trends in Neurosciences, 19 (1996), 130-137.
    [7] Neural Computation, 24 (2012), 2318-2345.
    [8] Journal of Neuroscience Methods, 183 (2009), 287-299.
    [9] Journal of Neuroscience Methods, 195 (2011), 92-106.
    [10] Journal of Neurophysiology, 109 (2013), 1457-1472.
    [11] Journal of Neuroscience Methods, 165 (2007), 151-161.
    [12] Annales de la Faculté des Sciences de Toulouse, 7 (1905), 265-315.
    [13] Frontiers in Cellular Neuroscience, 8 (2014), 452.
    [14] Journal of Computational Neuroscience, 14 (2003), 239-251.
    [15] Neural Computation, 26 (2014), 306-348.
    [16] Current Opinion in Neurobiology, 4 (1994), 569-579.
    [17] Journal of Neuroscience, 18 (1998), 3870-3896.
    [18] Neural Computation, 4 (1992), 643-646.
    [19] Journal of Neuroscience, 13 (1993), 334-350.
    [20] Current Opinion in Neurobiology, 15 (2005), 585-592.
    [21] Network: Computation in Neural Systems, 8 (1997), 127-164.
    [22] in Analysis of Parallel Spike Trains (eds. S. Grün and S. Rotter), Springer Series in Computational Neuroscience, 7, Springer US, Boston, MA, 2010, 129-156.
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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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