Interspike Interval Statistics in the Stochastic Hodgkin-Huxley Model: Coexistence of Gamma Frequency Bursts and Highly Irregular Firing

Authors:
Peter Rowat
Affiliations:
Institute for Neural Computation, University of California at San Diego, La Jolla, CA 92093, U.S.A. prowat@ucsd.edu
Venue:
Neural Computation
Year:
2007

Citing 6
Cited 4

Analysis of neural excitability and oscillations

Methods in neuronal modeling
Physiological gain leads to high ISI variability in a simple model of a cortical regular spiking cell

Neural Computation
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator

ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Dynamics of membrane excitability determine interspike interval variability: a link between spike generation mechanisms and cortical spike train statistics

Neural Computation
Ion channel stochasticity may be critical in determining the reliability and precision of spike timing

Neural Computation
Reliability of spike timing is a general property of spiking model neurons

Neural Computation

Irregular firing of isolated cortical interneurons in vitro driven by intrinsic stochastic mechanisms

Neural Computation
Discrimination with spike times and isi distributions

Neural Computation
Identification and continuity of the distributions of burst-length and interspike intervals in the stochastic morris-lecar neuron

Neural Computation
Reduction of stochastic conductance-based neuron models with time-scales separation

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.02

Visualization

Abstract

When the classical Hodgkin-Huxley equations are simulated with Na- and K-channel noise and constant applied current, the distribution of interspike intervals is bimodal: one part is an exponential tail, as often assumed, while the other is a narrow gaussian peak centered at a short interspike interval value. The gaussian arises from bursts of spikes in the gamma-frequency range, the tail from the interburst intervals, giving overall an extraordinarily high coefficient of variation---up to 2.5 for 180,000 Na channels when I ≈ 7μAcm2. Since neurons with a bimodal ISI distribution are common, it may be a useful model for any neuron with class 2 firing. The underlying mechanism is due to a subcritical Hopf bifurcation, together with a switching region in phase-space where a fixed point is very close to a system limit cycle. This mechanism may be present in many different classes of neurons and may contribute to widely observed highly irregular neural spiking.