Mathematical Biosciences and Engineering, 2016, 13(3): 551-568. doi: 10.3934/mbe.2016007.

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

Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

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

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.
  Figure/Table
  Supplementary
  Article Metrics

Keywords spontaneous activity; latency coding; renewal process; Fisher information; neuroscience.

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

References

  • 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, Journal of Mathematical Psychology, 2016, 71, 22, 10.1016/j.jmp.2016.02.006
  • 2. Marie Levakova, Efficiency of rate and latency coding with respect to metabolic cost and time, Biosystems, 2017, 161, 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, Neural Computation, 2016, 28, 10, 2162, 10.1162/NECO_a_00880

Reader Comments

your name: *   your email: *  

Copyright Info: 2016, Marie Levakova, licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved