Fluctuation phenomena in nerve membrane

Verveen, A. A.; Derksen, H. E.

doi:10.1109/proc.1968.6443

Cited by 152 publications

(90 citation statements)

References 40 publications

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“…The spike timing of the SGN inherits variability from the synaptic transmission process between an IHC that is characterized by probabilistic release of vesicles (Glowatzki and Fuchs 2002;Heil et al 2007;Safieddine et al 2012). However, the spike timing of the SGN is known to have intrinsic variability due to the inherent stochasticity of voltage-gated ion channel fluctuations (Verveen and Derksen 1968;Sigworth 1981). While smaller in magnitude than IHC vesicle release variability, the effect of ion channel fluctuations in SGNs is larger than for many other types of neurons since membrane noise is greater at small node of Ranvier diameters (Verveen 1962).…”

Section: Spatial Effects Of CI Stimulation Related To Temporal Interamentioning

confidence: 99%

Temporal Considerations for Stimulating Spiral Ganglion Neurons with Cochlear Implants

Boulet

White

Bruce

2015

JARO

103

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A wealth of knowledge about different types of neural responses to electrical stimulation has been developed over the past 100 years. However, the exact forms of neural response properties can vary across different types of neurons. In this review, we survey four stimulus-response phenomena that in recent years are thought to be relevant for cochlear implant stimulation of spiral ganglion neurons (SGNs): refractoriness, facilitation, accommodation, and spike rate adaptation. Of these four, refractoriness is the most widely known, and many perceptual and physiological studies interpret their data in terms of refractoriness without incorporating facilitation, accommodation, or spike rate adaptation. In reality, several or all of these behaviors are likely involved in shaping neural responses, particularly at higher stimulation rates. A better understanding of the individual and combined effects of these phenomena could assist in developing improved cochlear implant stimulation strategies. We review the published physiological data for electrical stimulation of SGNs that explores these four different phenomena, as well as some of the recent studies that might reveal the biophysical bases of these stimulus-response phenomena.

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Section: Spatial Effects Of CI Stimulation Related To Temporal Interamentioning

confidence: 99%

Temporal Considerations for Stimulating Spiral Ganglion Neurons with Cochlear Implants

Boulet

White

Bruce

2015

JARO

103

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“…Current noise is however shown to have two distinct deficiencies (Goldwyn and Shea-Brown 2011). The first deficiency is independence of the membrane voltage even though the noise intensity has been shown to be voltage dependent (Verveen and Derksen 1968). For the purposes of this study, noise intensity is regarded as a measure of the magnitude of the membrane noise amplitude of which the standard deviation is the rms noise.…”

Section: Introductionmentioning

confidence: 97%

“…One of the important properties of a nerve fibre affecting temporal characteristics such as latency is variance in threshold stimulus intensity (Hales et al 2004;Javel et al 1987;Verveen 1962;Verveen and Derksen 1968). This activation threshold variability has been shown to be primarily caused by the stochastic behaviour of the node of Ranvier's sodium channels (Hales et al 2004) or as noted by Sigworth (1980) and Rubinstein (1995): the macroscopic fluctuation of threshold can sufficiently be accounted for by the microscopic fluctuations of the voltage dependent sodium channels.…”

Section: Introductionmentioning

confidence: 99%

“…This activation threshold variability has been shown to be primarily caused by the stochastic behaviour of the node of Ranvier's sodium channels (Hales et al 2004) or as noted by Sigworth (1980) and Rubinstein (1995): the macroscopic fluctuation of threshold can sufficiently be accounted for by the microscopic fluctuations of the voltage dependent sodium channels. The intensity of these microscopic fluctuations, also referred to as membrane noise, in turn has been shown to be a function of membrane voltage (Verveen and Derksen 1968). Numerous approaches are followed to account for the threshold variability in both conductance based (Dangerfield et al 2012;Goldwyn and SheaBrown 2011;Huang et al 2013;Rattay et al 2001b;Rubinstein 1995) and phenomenological (Bruce et al 1999a;1999b;Macherey et al 2007) stochastic models.…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between activation or discharge probability and stimulus intensity in the stochastic nerve has experimentally been shown to be Gaussian (Javel et al 1987;Shepherd and Javel 1997;Verveen 1962;Verveen and Derksen 1968). Both the threshold (mean, μ) and spread (standard deviation, σ) of the Gaussian discharge probability function (DPF) proved dependent on stimulus duration, but the relative spread ( ) was found to be independent of stimulus duration for pulse durations between 100 μs and 3 ms. RS thus characterises the excitability fluctuation as a measure of the threshold region width or spread (σ) relative to the mean threshold value (μ) (Rubinstein 1995;Verveen 1962).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of a voltage-dependent current noise algorithm for conductance-based stochastic modelling of auditory nerve fibres

Badenhorst

Hanekom

2016

Biol Cybern

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The study presents the development of an alternative noise current term and novel voltage dependent current noise algorithm for conductance based stochastic auditory nerve fibre (ANF) models. ANFs are known to have significant variance in threshold stimulus which affects temporal characteristics such as latency. This variance is primarily caused by the stochastic behaviour or microscopic fluctuations of the node of Ranvier's voltage dependent sodium channels of which the intensity is a function of membrane voltage. Though easy to implement and low in computational cost, existing current noise models have two deficiencies: it is independent of membrane voltage and it is unable to inherently determine the noise intensity required to produce in vivo measured discharge probability functions. The proposed algorithm overcomes these deficiencies whilst maintaining its low computational cost and ease of implementation compared to other conductance and Markovian based stochastic models. The algorithm is applied to a Hodgkin-Huxley based compartmental cat ANF model and validated via comparison of the threshold probability and latency distributions to measured cat ANF data. Simulation results show the algorithm's adherence to in vivo stochastic fibre characteristics such as an exponential relationship between the membrane noise and transmembrane voltage, a negative linear relationship between the log of the relative spread of the discharge probability and the log of the fibre diameter and a decrease in latency with an increase in stimulus intensity.

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Some engineering models for interactions of electric and magnetic fields with biological systems

Barnes

1992

Bioelectromagnetics

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The objective of this paper is to review some of the fundamental mechanisms for the interaction of electric and magnetic fields with biological systems at variable levels of field strengths and to examine several possible ways by which weak fields may influence these systems. We begin with a review of the basic equations by which electric or magnetic fields interact with biological fluids and follow it with a look at the effects of inserting a simple cell membrane. The initial starting points are the force equations on charged particles and dipoles. We examine their effects on current flow, the orientation of long-chain molecules, and the forces which can be exerted by particles of magnetite on membranes. This is followed by a very simple model for the effects of a cell membrane on the overall current distribution and a model for current flow through a membrane. Some sources of nonlinearities which might serve as mechanisms for converting weak electrical signals from one frequency to a more biologically significant frequency are described. Additionally, three models by which a biological system may extract weak signals from noise are presented. The first of these is the injection-locking of oscillating processes where the signal-to-noise ratio may be less than unity. The second is parametric amplification which allows the external signal and the biological process to be at different frequencies and where stability requirements on the external pump frequency discriminates against the noise. The third approach is to examine a computer model for a neural network which can be trained to identify a 60 Hz field at signal-to-noise ratios much less than one. The key to each of these models for possible interactions of magnetic fields with biological systems is the long-term coherence of the signal with respect to the noise. Finally, we briefly examine the possibility of using scanning force and tunneling microscopes to give a better description of the characteristics of the cell surface.

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Fluctuation phenomena in nerve membrane

Cited by 152 publications

References 40 publications

Temporal Considerations for Stimulating Spiral Ganglion Neurons with Cochlear Implants

Temporal Considerations for Stimulating Spiral Ganglion Neurons with Cochlear Implants

Development of a voltage-dependent current noise algorithm for conductance-based stochastic modelling of auditory nerve fibres

Some engineering models for interactions of electric and magnetic fields with biological systems

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