Literature DB >> 17009486

Effects of neural refractoriness on spatio-temporal variability in spike initiations with Electrical stimulation.

Hiroyuki Mino1, Jay T Rubinstein.   

Abstract

In this paper, the effects of neural refractoriness on action potential (spike) initiations with electrical stimulation are investigated using computer modeling and simulation techniques. The computational model was composed of a myelinated nerve fiber with 50 nodes of Ranvier, each consisting of stochastic sodium and potassium channels, making it possible to represent the fluctuations of spike initiation. A series of two-pulse stimuli was presented by a stimulating electrode above the central (26th) node of Ranvier. The amplitude of the first (masker) pulse stimulus was set such that the masker pulse stimulus evoked spikes on each trial, while that of the second (probe) pulse stimulus was set such that the probe pulse stimulus evoked spikes on a half of trials, threshold values. Then the transmembrane potentials in response to the probe pulse stimulus were recorded at each node (i.e., 1-50 nodes) in order to determine the spike initiation node and time. From the observation of the spike initiation node and time, a spatio-temporal histogram as well as a spatial variability and a temporal variability of spike initiations was generated which allowed us to interpret fluctuations in spike initiation node and time. It was shown that the distribution of spike initiations tended to become greater spatially and longer temporally as the masker-probe intervals (MPIs) of the two-pulse stimuli shortened. It was also shown that the number of activated sodium channels as functions of space and time tended to become smaller due to inactivation of sodium channels and varied spatially and temporally as MPIs shortened. These findings may imply that the stochastic sodium channels during a relative refractory period may contribute to enhancing the fluctuations in spike initiations, and give us an insight into encoding information with electric stimuli to improve the performance of the prosthetic devices, especially cochlear implants.

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Year:  2006        PMID: 17009486     DOI: 10.1109/TNSRE.2006.881590

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  9 in total

1.  A point process framework for modeling electrical stimulation of the auditory nerve.

Authors:  Joshua H Goldwyn; Jay T Rubinstein; Eric Shea-Brown
Journal:  J Neurophysiol       Date:  2012-06-06       Impact factor: 2.714

2.  Changes across time in the temporal responses of auditory nerve fibers stimulated by electric pulse trains.

Authors:  Charles A Miller; Ning Hu; Fawen Zhang; Barbara K Robinson; Paul J Abbas
Journal:  J Assoc Res Otolaryngol       Date:  2008-01-17

3.  Temporal Response Properties of the Auditory Nerve in Implanted Children with Auditory Neuropathy Spectrum Disorder and Implanted Children with Sensorineural Hearing Loss.

Authors:  Shuman He; Paul J Abbas; Danielle V Doyle; Tyler C McFayden; Stephen Mulherin
Journal:  Ear Hear       Date:  2016 Jul-Aug       Impact factor: 3.570

4.  The dependence of auditory nerve rate adaptation on electric stimulus parameters, electrode position, and fiber diameter: a computer model study.

Authors:  Jihwan Woo; Charles A Miller; Paul J Abbas
Journal:  J Assoc Res Otolaryngol       Date:  2009-12-22

5.  Temporal response properties of the auditory nerve: data from human cochlear-implant recipients.

Authors:  Michelle L Hughes; Erin E Castioni; Jenny L Goehring; Jacquelyn L Baudhuin
Journal:  Hear Res       Date:  2012-02-08       Impact factor: 3.208

6.  Neural Adaptation of the Electrically Stimulated Auditory Nerve Is Not Affected by Advanced Age in Postlingually Deafened, Middle-aged, and Elderly Adult Cochlear Implant Users.

Authors:  Shuman He; Jeffrey Skidmore; Sara Conroy; William J Riggs; Brittney L Carter; Ruili Xie
Journal:  Ear Hear       Date:  2022-01-03       Impact factor: 3.562

7.  Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.

Authors:  Daniele Linaro; Marco Storace; Michele Giugliano
Journal:  PLoS Comput Biol       Date:  2011-03-10       Impact factor: 4.475

8.  Kilohertz Frequency Deep Brain Stimulation Is Ineffective at Regularizing the Firing of Model Thalamic Neurons.

Authors:  João Couto; Warren M Grill
Journal:  Front Comput Neurosci       Date:  2016-03-15       Impact factor: 2.380

Review 9.  The Electrically Evoked Compound Action Potential: From Laboratory to Clinic.

Authors:  Shuman He; Holly F B Teagle; Craig A Buchman
Journal:  Front Neurosci       Date:  2017-06-23       Impact factor: 4.677

  9 in total

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