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Journal of Computational Neuroscience

Erschienen in:

01.08.2010

Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models

verfasst von: Shinsuke Koyama, Liam Paninski

Erschienen in: Journal of Computational Neuroscience | Ausgabe 1-2/2010

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Abstract

A number of important data analysis problems in neuroscience can be solved using state-space models. In this article, we describe fast methods for computing the exact maximum a posteriori (MAP) path of the hidden state variable in these models, given spike train observations. If the state transition density is log-concave and the observation model satisfies certain standard assumptions, then the optimization problem is strictly concave and can be solved rapidly with Newton–Raphson methods, because the Hessian of the loglikelihood is block tridiagonal. We can further exploit this block-tridiagonal structure to develop efficient parameter estimation methods for these models. We describe applications of this approach to neural decoding problems, with a focus on the classic integrate-and-fire model as a key example.

Vorheriger Artikel Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control

Nächster Artikel A new look at state-space models for neural data

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Titel: Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models
verfasst von: Shinsuke Koyama
Liam Paninski
Publikationsdatum: 01.08.2010
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 1-2/2010
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-009-0150-x

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