Loading [Contrib]/a11y/accessibility-menu.js
Search Advanced

Mathematical Biosciences and Engineering

2016, Volume 13, Issue 3: 551-568. doi: 10.3934/mbe.2016007
Previous Article Next Article

Effect of spontaneous activity on stimulus detection in a simple neuronal model

  • 1.
    Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Kotlarska 2a, 611 37 Brno
  • Received: 01 March 2015 Accepted: 29 June 2018 Published: 01 January 2016

  • MSC : Primary: 62F10, 62P10; Secondary: 60K05.

  • It is studied what level of a continuous-valued signal is optimally estimable on the basis of first-spike latency neuronal data. When a spontaneous neuronal activity is present, the first spike after the stimulus onset may be caused either by the stimulus itself, or it may be a result of the prevailing spontaneous activity. Under certain regularity conditions, Fisher information is the inverse of the variance of the best estimator. It can be considered as a function of the signal intensity and then indicates accuracy of the estimation for each signal level. The Fisher information is normalized with respect to the time needed to obtain an observation. The accuracy of signal level estimation is investigated in basic discharge patterns modelled by a Poisson and a renewal process and the impact of the complex interaction between spontaneous activity and a delay of the response is shown.

    Citation: Marie Levakova. Effect of spontaneous activity on stimulus detection in a simple neuronal model[J]. Mathematical Biosciences and Engineering, 2016, 13(3): 551-568. doi: 10.3934/mbe.2016007

    Related Papers:

    [1] Sven Blankenburg, Benjamin Lindner . The effect of positive interspike interval correlations on neuronal information transmission. Mathematical Biosciences and Engineering, 2016, 13(3): 461-481. doi: 10.3934/mbe.2016001
    [2] Shinsuke Koyama, Lubomir Kostal . The effect of interspike interval statistics on the information gainunder the rate coding hypothesis. Mathematical Biosciences and Engineering, 2014, 11(1): 63-80. doi: 10.3934/mbe.2014.11.63
    [3] Guixiang Liang, Xiang Li, Hang Yuan, Min Sun, Sijun Qin, Benzheng Wei . Abnormal static and dynamic amplitude of low-frequency fluctuations in multiple brain regions of methamphetamine abstainers. Mathematical Biosciences and Engineering, 2023, 20(7): 13318-13333. doi: 10.3934/mbe.2023593
    [4] Gayathri Vivekanandhan, Hamid Reza Abdolmohammadi, Hayder Natiq, Karthikeyan Rajagopal, Sajad Jafari, Hamidreza Namazi . Dynamic analysis of the discrete fractional-order Rulkov neuron map. Mathematical Biosciences and Engineering, 2023, 20(3): 4760-4781. doi: 10.3934/mbe.2023220
    [5] Virginia Giorno, Serena Spina . On the return process with refractoriness for a non-homogeneous Ornstein-Uhlenbeck neuronal model. Mathematical Biosciences and Engineering, 2014, 11(2): 285-302. doi: 10.3934/mbe.2014.11.285
    [6] Sarah Kadelka, Stanca M Ciupe . Mathematical investigation of HBeAg seroclearance. Mathematical Biosciences and Engineering, 2019, 16(6): 7616-7658. doi: 10.3934/mbe.2019382
    [7] Andrey Olypher, Jean Vaillant . On the properties of input-to-output transformations in neuronal networks. Mathematical Biosciences and Engineering, 2016, 13(3): 579-596. doi: 10.3934/mbe.2016009
    [8] Jianzhong Peng, Wei Zhu, Qiaokang Liang, Zhengwei Li, Maoying Lu, Wei Sun, Yaonan Wang . Defect detection in code characters with complex backgrounds based on BBE. Mathematical Biosciences and Engineering, 2021, 18(4): 3755-3780. doi: 10.3934/mbe.2021189
    [9] Choonsung Shin, Sung-Hee Hong, Hieyoung Jeong, Hyoseok Yoon, Byoungsoo Koh . All-in-one encoder/decoder approach for non-destructive identification of 3D-printed objects. Mathematical Biosciences and Engineering, 2022, 19(12): 14102-14115. doi: 10.3934/mbe.2022657
    [10] Aniello Buonocore, Luigia Caputo, Enrica Pirozzi, Maria Francesca Carfora . A simple algorithm to generate firing times for leaky integrate-and-fire neuronal model. Mathematical Biosciences and Engineering, 2014, 11(1): 1-10. doi: 10.3934/mbe.2014.11.1
  • It is studied what level of a continuous-valued signal is optimally estimable on the basis of first-spike latency neuronal data. When a spontaneous neuronal activity is present, the first spike after the stimulus onset may be caused either by the stimulus itself, or it may be a result of the prevailing spontaneous activity. Under certain regularity conditions, Fisher information is the inverse of the variance of the best estimator. It can be considered as a function of the signal intensity and then indicates accuracy of the estimation for each signal level. The Fisher information is normalized with respect to the time needed to obtain an observation. The accuracy of signal level estimation is investigated in basic discharge patterns modelled by a Poisson and a renewal process and the impact of the complex interaction between spontaneous activity and a delay of the response is shown.


    [1] Neural Comput., 11 (1999), 91-101.
    [2] J. Neurosci., 48 (1982), 217-237.
    [3] Neural Comput., 17 (2005), 839-858.
    [4] Neural Comput., 13 (2001), 1351-1377.
    [5] Proc. Natl. Acad. Sci. USA, 94 (1997), 5411-5416.
    [6] Neural Comput., 14 (2002), 2317-2351.
    [7] J. Neurosci., 32 (2012), 2998-3008.
    [8] Neural Comput., 10 (1998), 1731-1757.
    [9] Chem. Senses, 23 (1998), 181-196.
    [10] Biophys. J., 71 (1996), 3013-3021.
    [11] Methuen, London, 1966.
    [12] Nature Neurosci., 1 (1998), 501-507.
    [13] Nature Neurosci., 8 (2005), 1684-1689.
    [14] Nature, 381 (1996), 610-613.
    [15] J. Opt. Soc. Am. A, 24 (2007), 1529-1537.
    [16] J. Neurophysiol., 80 (1998), 2151-2160.
    [17] J. Neurosci. Meth., 83 (1998), 185-194.
    [18] J. Neurosci., 20 (2000), 1216-1228.
    [19] J. Neurophysiol., 87 (2002), 1749-1762.
    [20] J. Neurophysiol., 76 (1996), 1356-1360.
    [21] Biophys. J., 4 (1964), 41-68.
    [22] IEEE Trans. Biomed. Engineering, 36, 4-14.
    [23] Biol. Cybern., 103 (2010), 43-56.
    [24] Biol. Cybern., 92 (2005), 199-205.
    [25] Phys. Rev. E, 60 (1999), 4687-4695.
    [26] Phys. Rev. Lett., 84 (2000), p4773.
    [27] Springer, New York, 2010.
    [28] Experientia, 51 (1995), 1003-1027.
    [29] J. Neurophysiol., 77 (1997), 2616-2641.
    [30] J. Neurophysiol., 97 (2007), 1078-1087.
    [31] Neurocomputing, 38 (2001), 239-248.
    [32] J. Comput. Neurosci., 16 (2004), 129-138.
    [33] PLoS Comput. Biol., 4 (2008), e1000053, 11pp.
    [34] Neural Comput., 27 (2015), 1051-1057.
    [35] Math. Biosci. Eng., 11 (2014), 63-80.
    [36] Neural Comput., 17 (2005), 2240-2257.
    [37] BioSystems, 89 (2007), 10-15.
    [38] Math. Biosci., 207 (2007), 261-274.
    [39] J. Peripher. Nerv. Syst., 4 (1998), 27-42.
    [40] Biol. Cybern., 108 (2014), 475-493.
    [41] BioSystems, 136 (2015), 23-24.
    [42] J. Comput. Neurosci., 19 (2005), 199-221.
    [43] Nature, 411 (2001), 698-701.
    [44] Hearing Res., 167 (2002), 13-27.
    [45] J. Neurosci., 25 (2005), 10049-10060.
    [46] Neural Comput., 22 (2010), 1675-1697.
    [47] Neuron, 29 (2001), 769-777.
    [48] Phil. Trans. R. Soc. B, 369 (2014), 20120467.
    [49] Neurosci. Res. Prog. Bull., 6 (1968), 221-348.
    [50] Neuron, 32 (2001), 503-514.
    [51] BioSystems, 67 (2002), 187-193.
    [52] J. Neurophysiol., 85 (2001), 1039-1050.
    [53] Eur. J. Neurosci., 18 (2003), 1135-1154.
    [54] Network, 7 (1996), 687-716.
    [55] Phys. Rev. E, 86 (2012), 021128.
    [56] BioSystems, 112 (2013), 249-257.
    [57] Ann. Math. Stat., 28 (1957), 362-377.
    [58] Neural Comp., 14 (2002), 155-189.
    [59] Hearing Res., 35 (1988), 165-190.

  • This article has been cited by:

    1. Lubomir Kostal, Stimulus reference frame and neural coding precision, 2016, 71, 00222496, 22, 10.1016/j.jmp.2016.02.006
    2. Marie Levakova, Efficiency of rate and latency coding with respect to metabolic cost and time, 2017, 161, 03032647, 31, 10.1016/j.biosystems.2017.06.005
    3. Marie Levakova, Massimiliano Tamborrino, Lubomir Kostal, Petr Lansky, Presynaptic Spontaneous Activity Enhances the Accuracy of Latency Coding, 2016, 28, 0899-7667, 2162, 10.1162/NECO_a_00880
    4. Guowei Wang, Yan Fu, Spatiotemporal patterns and collective dynamics of bi-layer coupled Izhikevich neural networks with multi-area channels, 2022, 20, 1551-0018, 3944, 10.3934/mbe.2023184
  • Reader Comments
  • © 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)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Mathematical Biosciences and Engineering
3.9

Metrics

Article views(2887) PDF downloads(478) Cited by(4)

Preview PDF
Download XML
Export Citation
Article outline
Abstract
References

Other Articles By Authors

Related pages

Tools

Copyright © AIMS Press

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog