Neural Comput - Online variational inference for state-space models with point-process observations.

Tópicos

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{ group(2977) signific(1463) compar(1072) }
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{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
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{ health(1844) social(1437) communiti(874) }
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{ high(1669) rate(1365) level(1280) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online filtering algorithm, which can also be used to track changes in physiological parameters. The methods are assessed on simulated data, and results are compared to expectation-maximization, as well as Monte Carlo estimation techniques, in order to evaluate the accuracy of the proposed approach. The VB filter is further assessed on a data set of taste-response neural cells, showing that the proposed approach can effectively capture dynamical changes in neural responses in real time.

Resumo Limpo

present variat bayesian vb approach state paramet infer statespac model pointprocess observ physiolog plausibl model signal process spike data also give deriv variat smoother well effici onlin filter algorithm can also use track chang physiolog paramet method assess simul data result compar expectationmaxim well mont carlo estim techniqu order evalu accuraci propos approach vb filter assess data set tasterespons neural cell show propos approach can effect captur dynam chang neural respons real time

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