Biomaterial Database

Spike trains in a stochastic Hodgkin-Huxley system. 2005

Tuckwell Henry C

Department of Mathematics, University of California San Diego, Gillman Drive, La Jolla, CA 92093, USA. htuckwel@math.ucsd.edu

We consider a standard Hodgkin-Huxley model neuron with a Gaussian white noise input current with drift parameter mu and variance parameter sigma(2). Partial differential equations of second order are obtained for the first two moments of the time taken to spike from (any) initial state, as functions of the initial values. The analytical theory for a 2-component (V,m) approximation is also considered. Let mu(c) (approximately 4.15) be the critical value of mu for firing when noise is absent. Large sample simulation results are obtained for mu<mu(c) and mu>mu(c), for many values of sigma between 0 and 25. For the time to spike, the 2-component approximation is accurate for all sigma when mu=10, for sigma>7 when mu=5 and only when sigma>15 when mu=2. When mu<mu(c), sigma must be large to induce firing so paths are always erratic. As the noise increases, the coefficient of variation (CV) has a well-defined minimum, and then climbs steadily over the range considered. If mu is just above mu(c), when the noise is small, paths are close to deterministic and the standard deviation and CV of the time to spike are small. As sigma increases, some very erratic paths (some almost oscillatory) appear, making the mean, standard deviation and CV of the spike time very large. These erratic paths start to have a large influence, so all three statistics have very pronounced maxima at intermediate sigma. When mu>>mu(c), most paths show similar behavior and the moments exhibit smoothly changing behavior as sigma increases. Thus there are a different number of regimes depending on the magnitude of mu relative to mu(c): one when mu is small and when mu is large; but three when mu is close to and above mu(c). Both for the Hodgkin-Huxley (HH) system and the 2-component approximation, and regardless of the value of mu, the CV tends to about 1.3 at the largest value (25) of sigma considered. We also discuss in detail the problem of determining the interspike interval and give an accurate method for estimating this random variable by decomposing the interval into stochastic and almost deterministic components.

UI	MeSH Term	Description	Entries
D008564	Membrane Potentials	The voltage differences across a membrane. For cellular membranes they are computed by subtracting the voltage measured outside the membrane from the voltage measured inside the membrane. They result from differences of inside versus outside concentration of potassium, sodium, chloride, and other ions across cells' or ORGANELLES membranes. For excitable cells, the resting membrane potentials range between -30 and -100 millivolts. Physical, chemical, or electrical stimuli can make a membrane potential more negative (hyperpolarization), or less negative (depolarization).	Resting Potentials,Transmembrane Potentials,Delta Psi,Resting Membrane Potential,Transmembrane Electrical Potential Difference,Transmembrane Potential Difference,Difference, Transmembrane Potential,Differences, Transmembrane Potential,Membrane Potential,Membrane Potential, Resting,Membrane Potentials, Resting,Potential Difference, Transmembrane,Potential Differences, Transmembrane,Potential, Membrane,Potential, Resting,Potential, Transmembrane,Potentials, Membrane,Potentials, Resting,Potentials, Transmembrane,Resting Membrane Potentials,Resting Potential,Transmembrane Potential,Transmembrane Potential Differences
D008959	Models, Neurological	Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.	Neurologic Models,Model, Neurological,Neurologic Model,Neurological Model,Neurological Models,Model, Neurologic,Models, Neurologic
D009474	Neurons	The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM.	Nerve Cells,Cell, Nerve,Cells, Nerve,Nerve Cell,Neuron
D002462	Cell Membrane	The lipid- and protein-containing, selectively permeable membrane that surrounds the cytoplasm in prokaryotic and eukaryotic cells.	Plasma Membrane,Cytoplasmic Membrane,Cell Membranes,Cytoplasmic Membranes,Membrane, Cell,Membrane, Cytoplasmic,Membrane, Plasma,Membranes, Cell,Membranes, Cytoplasmic,Membranes, Plasma,Plasma Membranes
D003198	Computer Simulation	Computer-based representation of physical systems and phenomena such as chemical processes.	Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000200	Action Potentials	Abrupt changes in the membrane potential that sweep along the CELL MEMBRANE of excitable cells in response to excitation stimuli.	Spike Potentials,Nerve Impulses,Action Potential,Impulse, Nerve,Impulses, Nerve,Nerve Impulse,Potential, Action,Potential, Spike,Potentials, Action,Potentials, Spike,Spike Potential
D000818	Animals	Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA.	Animal,Metazoa,Animalia
D001683	Biological Clocks	The physiological mechanisms that govern the rhythmic occurrence of certain biochemical, physiological, and behavioral phenomena.	Biological Oscillators,Oscillators, Endogenous,Pacemakers, Biological,Biologic Clock,Biologic Oscillator,Biological Pacemakers,Clock, Biologic,Clocks, Biological,Oscillator, Biologic,Oscillators, Biological,Pacemaker, Biologic,Pacemakers, Biologic,Biologic Clocks,Biologic Oscillators,Biologic Pacemaker,Biologic Pacemakers,Biological Clock,Biological Oscillator,Biological Pacemaker,Clock, Biological,Clocks, Biologic,Endogenous Oscillator,Endogenous Oscillators,Oscillator, Biological,Oscillator, Endogenous,Oscillators, Biologic,Pacemaker, Biological
D013269	Stochastic Processes	Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.	Process, Stochastic,Stochastic Process,Processes, Stochastic