Journal of Computational Neuroscience

, Volume 17, Issue 2, pp 107–126 | Cite as

Tristate Markov Model for the Firing Statistics of Rapidly-Adapting Mechanoreceptive Fibers

  • Burak Güçlü
  • Stanley J. Bolanowski


Rapidly-adapting (RA) mechanoreceptive fibers, which are associated with Meissner corpuscles, mediate one component of the neural information that contributes to the sense of touch. Responses of cat RA fibers subject to 40-Hz sinusoidal stimulation were modeled as a Markov process. Since an RA fiber generates one, two or no spikes in each cycle of the stimulus, the fiber's activity was considered to exist in one of these three possible states. By analyzing empirically generated spike trains, the probability of each state and the probabilities of transitions between the three states were found as a function of the average firing rate of the fiber. The average firing rate depends on the stimulus amplitude. In addition, the phase of each spike with respect to the stimulus cycle was represented by a Laplace distribution. Based on empirical data, the mean and the standard deviation of this distribution decrease as the stimulus amplitude is increased. The entire stochastic model was implemented on a computer to simulate the responses of RA fibers. The post-stimulus time, inter-spike interval and period histograms generated from the simulations match the histograms obtained from the empirical data well as quantified by relative errors. This temporal model can be combined with a population model for average rate to derive a spatio-temporal description of the responses of somatosensory afferents. The effects of changing the stimulation frequency are discussed.

mechanoreceptor rapidly-adapting fiber Meissner corpuscle Markov process sinusoidal response 


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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Burak Güçlü
    • 1
    • 2
  • Stanley J. Bolanowski
    • 1
    • 2
  1. 1.Institute for Sensory ResearchSyracuse
  2. 2.Department of Bioengineering and NeuroscienceSyracuse UniversitySyracuseUSA

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