Changes in interspike interval during propagation: Quantitative description

George, Stephen A.

doi:10.1007/bf00366592

Cited by 28 publications

(15 citation statements)

References 9 publications

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“…This effect lengthens very short ISIs between their generation at the encoding site and their arrival at the synaptic arbors of the decoding site, while leaving longer ISIs unchanged. Although this effect has been reported previously [7], [9], [11]–[13], [32], [33], our study is the first to demonstrate it with action potentials generated by sensory stimulation rather than current injection. Since the distribution of ISIs is dependent on the stimulus (and the magnitude of the change in propagation velocity is in turn dependent on preceding ISI), our use of sensory stimulation allows us to draw stronger inferences about the relevance of this effect on neural coding in naturally behaving animals.…”

Section: Resultssupporting

confidence: 49%

“…Like the deceleration due to the relative refractory period, this effective acceleration of second spikes has also been observed before [8], [11], [14], [33], and is thought to be the result of an activity-dependent accumulation of potassium in the extracellular space around unmyelinated axons. We fit propagation time as a function of preceding ISI with a sum-of-exponentials (methods Eq.…”

Section: Resultsmentioning

confidence: 75%

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Information Transmission in Cercal Giant Interneurons Is Unaffected by Axonal Conduction Noise

2012

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What are the fundamental constraints on the precision and accuracy with which nervous systems can process information? One constraint must reflect the intrinsic “noisiness” of the mechanisms that transmit information between nerve cells. Most neurons transmit information through the probabilistic generation and propagation of spikes along axons, and recent modeling studies suggest that noise from spike propagation might pose a significant constraint on the rate at which information could be transmitted between neurons. However, the magnitude and functional significance of this noise source in actual cells remains poorly understood. We measured variability in conduction time along the axons of identified neurons in the cercal sensory system of the cricket Acheta domesticus, and used information theory to calculate the effects of this variability on sensory coding. We found that the variability in spike propagation speed is not large enough to constrain the accuracy of neural encoding in this system.

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Section: Resultssupporting

confidence: 49%

Section: Resultsmentioning

confidence: 75%

Information Transmission in Cercal Giant Interneurons Is Unaffected by Axonal Conduction Noise

2012

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“…Poisson trains have been used before in Hodgkin-Huxley model axons to characterize spike delay and velocity (George, 1977; Moradmand and Goldfinger, 1995), but not commonly in model axons with more complex excitability. We therefore examined the history-dependence of conduction delay in the PD neuron model axon by stimulating one end of the axon and measuring conduction delay between two positions along its length (Figure 1).…”

Section: Resultsmentioning

confidence: 99%

“…In addition to the basic ionic mechanisms necessary for spike propagation, i.e., fast sodium and delayed rectifier potassium conductances (Hodgkin and Huxley, 1952a, 1952b), axons can have a large variety of voltage-gated ion channels and ion pumps (Krishnan et al, 2009; Bucher and Goaillard, 2011; Debanne et al, 2011). As the activation and gating of these currents can be associated with a wide range of time constants, conduction velocity can change depending on the history of activity at timescales far exceeding refractory effects occurring in the millisecond range (George, 1977; Raymond, 1979; Weidner et al, 2002; Bucher and Goaillard, 2011; Ballo et al, 2012). However, the ionic mechanisms underlying history-dependence of spike propagation are not well understood, and the common approach to test excitability changes with simple paired conditioning and test pulses (Bostock et al, 1998; Krishnan et al, 2009; Bucher and Goaillard, 2011) is insufficient to capture slow dynamics and transformation of complex temporal patterns (Weidner et al, 2002; Ballo et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Ionic mechanisms underlying history-dependence of conduction delay in an unmyelinated axon

Zhang

Bucher

Nadim

2017

eLife

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Axonal conduction velocity can change substantially during ongoing activity, thus modifying spike interval structures and, potentially, temporal coding. We used a biophysical model to unmask mechanisms underlying the history-dependence of conduction. The model replicates activity in the unmyelinated axon of the crustacean stomatogastric pyloric dilator neuron. At the timescale of a single burst, conduction delay has a non-monotonic relationship with instantaneous frequency, which depends on the gating rates of the fast voltage-gated Na+ current. At the slower timescale of minutes, the mean value and variability of conduction delay increase. These effects are because of hyperpolarization of the baseline membrane potential by the Na+/K+ pump, balanced by an h-current, both of which affect the gating of the Na+ current. We explore the mechanisms of history-dependence of conduction delay in axons and develop an empirical equation that accurately predicts this history-dependence, both in the model and in experimental measurements.DOI: http://dx.doi.org/10.7554/eLife.25382.001

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“…Furthermore, when conduction velocity changes with activity, it is likely that there are correlated threshold changes, since such changes, distributed along the fibre, result in altered velocity of impulse propagation. In a variety of investigations, threshold changes are implicated from the conduction velocity measurements (Bullock, 1951;Gardner-Medwin, 1972; Kocsis, Vander Maelen & Kitai, 1977;George, 1977), and one variable can often be used as a measure of the other.…”

Section: Ubiquity Of Threshold Oscillationsmentioning

confidence: 99%

Effects of nerve impulses on threshold of frog sciatic nerve fibres.

Raymond¹

1979

The Journal of Physiology

127

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SUMMARY1. The firing thresholds of single myelinated fibres of frog sciatic nerves were monitored as a function of impulse activity in the fibre. The threshold was given by the number of coulombs in current pulses that excited a particular fibre half the time when delivered to the whole nerve. Threshold was tracked by a device that incrementally decreased the number of coulombs in the current pulse whenever the fibre responded and increased the pulse if it did not respond.2. There was a pattern to the after-oscillations of threshold following activity. The fibres were briefly refractory, transiently superexcitable for about 1-1-5 sec and then entered a phase of raised threshold or 'depression' that lasted for many minutes.3. Activity produced little change in the threshold curve during the refractory period. Strong depressions following prolonged activity prevented the threshold from returning to the base-line level within the time associated with the refractory period for the same fibre at rest.4. After an impulse, superexcitability reached a maximum within 7-20 msec. This peak was larger as the number of impulses in a preceding burst increased and as the intervals between the impulses became briefer. Each successive impulse of a burst contributed less to the growth of superexcitability, and after the burst had 6-10 impulses additional impulses contributed nothing.5. The depression phase was marked by the interaction between build-up, which depended on the activity rate, and recovery, which required as long as an hour or more for the threshold to be completely restored to resting level. These two mechanisms, one causing build-up and the other recovery, led to formation of dynamic equilibria. The threshold level at equilibrium increased monotonically with the activity rate.6. The processes associated with superexcitability interact with those producing depression. In active fibres showing raised thresholds, impulses are followed by a relative superexcitability that persists for at least as long as an absolute superexcitability (with threshold below the resting level) can be measured in the same fibre at rest.7. The duration of the superexcitable phase interpreted as a relative change in excitability was roughly the same regardless of the level of depression.8. The magnitude of the oscillation in threshold was five to ten times larger than the grey region (the range of stimuli for which response is probabilistic). It is 0022-3751/79/3290-0523 $01.50

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Changes in interspike interval during propagation: Quantitative description

Cited by 28 publications

References 9 publications

Information Transmission in Cercal Giant Interneurons Is Unaffected by Axonal Conduction Noise

Information Transmission in Cercal Giant Interneurons Is Unaffected by Axonal Conduction Noise

Ionic mechanisms underlying history-dependence of conduction delay in an unmyelinated axon

Effects of nerve impulses on threshold of frog sciatic nerve fibres.

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