Biomaterial Database

Analysis of the activity of single neurons in stochastic settings. 1988

I Nelken

Department of Physiology, Hebrew University Medical School, Jerusalem, Israel.

This paper presents a new way of modeling the activity of single neurons in stochastic settings. It incorporates in a natural way many physiological mechanisms not usually found in stochastic models, such as spatial integration, non-linear membrane characteristics and non-linear interactions between excitation and inhibition. The model is based on the fact that most of the neuronal inputs have a finite lifetime. Thus, the stochastic input can be modeled as a simple finite markov chain, and the membrane potential becomes a function of the state of this chain. Firing occurs at states whose membrane potential is above threshold. The main mathematical results of the model are: (i) the input-output firing rate curve is convex at low firing rates and is saturated at high firing rates, and (ii) at low firing rates, firing usually occurs when there is synchronous convergence of many excitatory events.

UI	MeSH Term	Description	Entries
D008390	Markov Chains	A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.	Markov Process,Markov Chain,Chain, Markov,Chains, Markov,Markov Processes,Process, Markov,Processes, Markov
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
D003491	Cybernetics	That branch of learning which brings together theories and studies on communication and control in living organisms and machines.	Cybernetic
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
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