Parameter estimation in a model for multidimensional recording of neuronal data: a Gibbsian approximation approach

This article proposes improved numerical procedures for estimating parameters in a spatiotemporal lattice model introduced for the analysis of cortical activities monitored from arrays of diodes. The numerical algorithms are based on approximations inspired by statistical physics. Both Gibbsian and...

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Bibliographic Details
Published in:	Biological cybernetics Vol. 89; no. 3; pp. 170 - 178
Main Authors:	Abdallahi, L M Ould Mohamed, La Rota, C, Béguin, M, François, O
Format:	Journal Article
Language:	English
Published:	Germany Springer Nature B.V 01-09-2003
Subjects:	Algorithms Animals Auditory Cortex - anatomy & histology Auditory Cortex - physiology Computer Simulation Guinea Pigs Models, Neurological Models, Statistical Monte Carlo Method Monte Carlo simulation Neurons - physiology Optics and Photonics Parameter estimation Reproducibility of Results Space Perception - physiology Studies Time Factors
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Summary:	This article proposes improved numerical procedures for estimating parameters in a spatiotemporal lattice model introduced for the analysis of cortical activities monitored from arrays of diodes. The numerical algorithms are based on approximations inspired by statistical physics. Both Gibbsian and mean-field approximations are used; they allow for computing local conditional probabilities inside the lattice. The statistical procedures rely on the computation of pseudomaximum-likelihood estimators. The estimators are evaluated on the basis of Monte Carlo simulations. These simulations show that mean-field approximations are useful for reducing the variance of estimators when the data are recorded from arrays of 144 diodes (which are in accordance with standard practice). In light of these improved methods, we give new interpretations for a data set obtained from optical recording of a Guinea pig's auditory cortex in response to pure tone stimulations.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0340-1200 1432-0770
DOI:	10.1007/s00422-003-0416-8