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

• Published in: IEEE transactions on biomedical engineering Vol. 48; no. 6; pp. 670 - 683
• Main Author: Bishop, W.B.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayes Theorem; Bayesian methods; Biological and medical sciences; Biophysical Phenomena; Biophysics; Current measurement; Damping; Excitatory Postsynaptic Potentials - physiology; Failure analysis; Fires; Fundamental and applied biological sciences. Psychology; Humans; In vitro; Isolated neuron and nerve. Neuroglia; Likelihood Functions; Long-Term Potentiation - physiology; Models, Neurological; Neurons; Neurotransmitters; Statistical distributions; Vertebrates: nervous system and sense organs; Voltage; Bayes estimation; Potentiation; Bernoulli trial; Electrical stimulus; Central nervous system; In vitro; Long term; Modeling; Neuron; Posterior probability; Synaptic transmission; Tetany; Excitatory postsynaptic potential; Brain (vertebrata)
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/10.923785

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.