Estimating the posterior probability of LTP failure by sequential Bayesian analysis of an imperfect Bernoulli trial model

Author

Bishop, William B.

Author_Institution

Lockheed Martin Electron. & Surveillance Syst., Manijus, NY, USA

Volume

48

Issue

6

fYear

2001

fDate

6/1/2001 12:00:00 AM

Firstpage

670

Lastpage

683

Abstract

A tetanically stimulated (TS) neuron is said to have failed to fire if its voltage-clamped excitatory postsynaptic current (EPSC) measurement is devoid of a long-term potentiation (LTP) response. This paper provides a method for evaluating the posterior probability of "failure" for TS neurons. A sequential Bayes algorithm is employed on an imperfect Bernoulli trial model to refine the posterior with each EPSC data record processed. The method is applied to both real and simulated LTP data and is shown to be consistent with the theoretical Beta-distributed posterior and the reported in vitro voltage-damped EPSC failure rates.

Keywords

Bayes methods; bioelectric phenomena; cellular transport; neurophysiology; physiological models; probability; excitatory postsynaptic current; imperfect Bernoulli trial model; in vitro voltage-damped EPSC failure rates; long-term potentiation failure; nerve cells synaptic transmission; posterior probability estimation; sequential Bayes algorithm; sequential Bayesian analysis; theoretical Beta-distributed posterior; Bayesian methods; Current measurement; Damping; Failure analysis; Fires; In vitro; Neurons; Neurotransmitters; Statistical distributions; Voltage; Algorithms; Bayes Theorem; Biophysics; Excitatory Postsynaptic Potentials; Humans; Likelihood Functions; Long-Term Potentiation; Models, Neurological;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/10.923785

Filename

923785