Neural Comput - A semiparametric Bayesian model for detecting synchrony among multiple neurons.

Tópicos

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Resumo

We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1s (spike) and 0s (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach are as follows. The nonparametric component (i.e., the gaussian process model) provides a flexible framework for modeling the underlying firing rates, and the parametric component (i.e., the copula model) allows us to make inferences regarding both contemporaneous and lagged relationships among neurons. Using the copula model, we construct multivariate probabilistic models by separating the modeling of univariate marginal distributions from the modeling of a dependence structure among variables. Our method is easy to implement using a computationally efficient sampling algorithm that can be easily extended to high-dimensional problems. Using simulated data, we show that our approach could correctly capture temporal dependencies in firing rates and identify synchronous neurons. We also apply our model to spike train data obtained from prefrontal cortical areas.

Resumo Limpo

propos scalabl semiparametr bayesian model captur depend among multipl neuron detect cofir possibl lag time pattern time discret time one spike interv result sequenc s spike s silenc neuron model use logist function continu latent variabl gaussian process prior multipl neuron correspond margin distribut coupl joint probabl distribut use parametr copula model advantag approach follow nonparametr compon ie gaussian process model provid flexibl framework model under fire rate parametr compon ie copula model allow us make infer regard contemporan lag relationship among neuron use copula model construct multivari probabilist model separ model univari margin distribut model depend structur among variabl method easi implement use comput effici sampl algorithm can easili extend highdimension problem use simul data show approach correct captur tempor depend fire rate identifi synchron neuron also appli model spike train data obtain prefront cortic area

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