4 Models for coarse temporal dependence 10 4.1 Cross-correlation histogram (CCH) . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Statistical hypothesis testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.3 Independent homogeneous Poisson process (HPP) model . . . . . . . . . . . . . 13 4.3.1 Bootstrap approximation . . . . . . . . . . . . . . . . . . . . . . . . . . ...

In this paper, Spike-and-Slab Dirichlet Process (SS-DP) priors are introduced and discussed for non-parametric Bayesian modeling and inference, especially in the mixture models context. Specifying a spike-and-slab base measure for DP priors combines the merits of Dirichlet process and spike-and-slab priors and serves as a flexible approach in Bayesian model selection and averaging. Computationa...

Neurons compute mainly with action potentials or “spikes”, which are stereotypical electrical impulses. Over the last century, the operating function of neurons has been mainly described in terms of firing rates, with the timing of spikes bearing little information. More recently, experimental evidence and theoretical studies have shown that the relative spike timing of inputs has an important ...

We propose V1 and MT functional models for biological motion recognition. Our V1 model transforms a video stream into spike trains through local motion detectors. The spike trains are the inputs of a spiking MT network. Each entity in the MT network corresponds to a simplified model of an MT cell. From the spike trains of MT cells a motion map of velocity distribution is built representing a se...

Generalized Linear Models (GLMs) have been used extensively in statistical models of spike train data. However, the maximum likelihood estimates model parameters and their uncertainty, can be challenging to compute situations where response non-response separated by a single predictor or linear combination multiple predictors. Such are likely arise many neural systems due properties such as ref...