Stochastic Simulations on the Reliability of Action Potential Propagation in Thin Axons

Faisal, A. Aldo; Laughlin, Simon B.

doi:10.1371/journal.pcbi.0030079

Cited by 125 publications

(127 citation statements)

References 45 publications

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“…A simulation study indicates that four distinct effects may corrupt propagating spike trains in thin axons: spikes being added, deleted, jittered, or split into subgroups (186). The local variation in the number of Na ϩ channels may cause microsaltatory conduction.…”

Section: B Delays Imposed By Axonal Irregularities and Ion Channelsmentioning

confidence: 99%

Axon Physiology

Debanne

Campanac

Bialowas

et al. 2011

Physiological Reviews

519

469

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Axons are generally considered as reliable transmission cables in which stable propagation occurs once an action potential is generated. Axon dysfunction occupies a central position in many inherited and acquired neurological disorders that affect both peripheral and central neurons. Recent findings suggest that the functional and computational repertoire of the axon is much richer than traditionally thought. Beyond classical axonal propagation, intrinsic voltage-gated ionic currents together with the geometrical properties of the axon determine several complex operations that not only control signal processing in brain circuits but also neuronal timing and synaptic efficacy. Recent evidence for the implication of these forms of axonal computation in the short-term dynamics of neuronal communication is discussed. Finally, we review how neuronal activity regulates both axon morphology and axonal function on a long-term time scale during development and adulthood.

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Section: B Delays Imposed By Axonal Irregularities and Ion Channelsmentioning

confidence: 99%

Axon Physiology

Debanne

Campanac

Bialowas

et al. 2011

Physiological Reviews

519

469

View full text Add to dashboard Cite

show abstract

“…Axons -nerve fibres -and the ion channels that amplify the electrical signals that pass along them are of molecular dimensions and require little energy. The diameter of a typical human axon, 0.1 micrometres, is so small that the signals it contains are susceptible to thermal noise and hence to random fluctuations 6 . Although such noise is often considered to hinder the operation of the nervous system, in some circumstances it might offer an advantage 7 .…”

Section: Wider Benefitsmentioning

confidence: 99%

Modelling: Build imprecise supercomputers

Palmer

2015

Nature

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Energy-optimized hybrid computers with a range of processor accuracies will advance modelling in fields from climate change to neuroscience, says Tim Palmer.T oday's supercomputers lack the power to model accurately many aspects of the real world, from the impact of cloud systems on Earth's climate to the processing ability of the human brain. Rather than wait decades for sufficiently powerful supercomputers -with their potentially unsustainable energy demands -it is time for researchers to reconsider the basic concept of the computer. We must move beyond the idea of a computer as a fast but otherwise traditional 'Turing machine' , churning through calculations bit by bit in a sequential, precise and reproducible manner.In particular, we should question whether all scientific computations need to be performed deterministically -that is, always producing the same output given the same input -and with the same high level of precision. I argue that for many applications they do not.Energy-efficient hybrid supercomputers with a range of processor accuracies need to be developed. These would combine conventional energy-intensive processors with low-energy, non-deterministic processors, able to analyse data at variable levels of precision. The demand for such machines could be substantial, across diverse sectors of the scientific community. MORE WITH LESSTake climate change, for example. Estimates of Earth's future climate are based on solving nonlinear (partial differential) equations for fluid flow in the atmosphere and oceans. Current climate simulators -typically with grid cells of 100 kilometres in width -can resolve the large, low-pressure weather systems typical of mid-latitudes, but not individual clouds. Yet modelling cloud systems accurately is crucial for reliable estimates of the impact of anthropogenic emissions on global temperature 1 .The resolution of this computational grid is determined by the available computing power. Current petaflop computers can perform up to 10 15 additions or multiplications -floating-point operationsper second (flops). By the early 2020s, next-generation exaflop supercomputers, capable of 10 18 operations per second, will be able to resolve the largest and most vigorous types of thunderstorm 2 . But cloud physics on scales smaller than a grid cell will still have to be approximated, or

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“…Whereas the point HH model has been the subject of a large number of studies and analyses, both with and without noise, as for example in Hassard (1978), Yu and Lewis (1989), Brown et al (1999), Tiesinga et al (2000), Tuckwell and Wan (2005), Lv (2007), Bruce (2007), Torcini et al (2007), and , there have been relatively few articles on the spatial system (e.g., Horikawa 1991;Faisal and Laughlin 2007).…”

Section: Introductionmentioning

confidence: 99%

The effects of various spatial distributions of weak noise on rhythmic spiking

Tuckwell

Jost

2010

J Comput Neurosci

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We consider the response of the classical Hodgkin-Huxley (HH) spatial system in the weak to intermediate noise regime near the bifurcation to repetitive spiking. The deterministic component of the input (signal) is restricted to a small segment near the origin whereas noise, with parameter σ, occurs either only in the signal region or throughout the whole neuron. In both cases small noise inhibits the spiking and there is a minimum in the spike counts at σ ≈ 0.15. At the same value of σ, the variance of the spike counts undergoes a pronounced maximum. For spatially restricted noise, the spike count continues to increase beyond the minimum until σ=0.5, but in the case of spatially extended noise the spike count begins to decline around σ=0.35 to give a local maximum. For both spatial distributions of noise, the variance of the spike count is found to also have a local minimum at about σ=0.4. Examples are given of the probability distributions of the spike counts and the spatial distributions of spikes with varying noise level. The differences in behaviours of the spike counts as noise increases beyond 0.3 are attributable to noise-induced spiking outside the signal region, which has a larger probability of occurrence when the noise is over an extended region. This aspect is investigated by ascertaining the probability of noise-induced spiking as a function of noise level and examination of the corresponding latency distributions. These findings prompt a definition of weak noise in the standard HH model as that for which the probability of secondary phenomena is negligible, which occurs when σ is less than about 0.3. Finally, if signal and weak (σ<0.3) noise are applied on disjoint intervals, then the noise has no effect on the instigation or propagation of spikes, no matter how large its region of application. These results are expected to apply to type 2 neurons in general, including the majority of cortical pyramidal cells.

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Stochastic Simulations on the Reliability of Action Potential Propagation in Thin Axons

Cited by 125 publications

References 45 publications

Axon Physiology

Axon Physiology

Modelling: Build imprecise supercomputers

The effects of various spatial distributions of weak noise on rhythmic spiking

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