TR available: Bayesian Methods and Compartmental Modeling

[Pierre Baldi]

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ON THE USE OF BAYESIAN METHODS FOR EVALUATING COMPARTMENTAL NEURAL MODELS

(40 pages = 35 pages + 5 figures)
(one figure is in color but should print OK in black and white)

P. Baldi, M. C. Vanier, and J. M. Bower
Department of Computation and Neural Systems
Caltech



ABSTRACT: In this TR, we provide a tutorial on Bayesian methods for
neurobiologists, as well an application of the methods to compartmental 
modeling. We first derive prior and likelihood functions for
compartmental neural models and for spike trains. We then apply the full
Bayesian inference machinery to parameter estimation, and model comparison
in the case of simple classes of compartmental models, with three and four
conductances. We also perform class comparison by approximating integrals
over the entire parameter space. Advantages and drawbacks are discussed.


Postscript and other problems reported to us have been corrected.