GP-Select: Accelerating EM Using Adaptive Subspace Preselection.

We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alternate between learning a selection function to reveal the relevant latent variables and using this to obtain a compact approximation of the posterior distribution for EM. This can make inference possible where the number of possible latent states is, for example, exponential in the number of latent variables, whereas an exact approach would be computationally infeasible.

Filtering with State-Observation Examples via Kernel Monte Carlo Filter.

This letter addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e.

Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys.

Pattern recognition methods have shown that functional magnetic resonance imaging (fMRI) data can reveal significant information about brain activity. For example, in the debate of how object categories are represented in the brain, multivariate analysis has been used to provide evidence of a distributed encoding scheme [Science 293:5539 (2001) 2425-2430]. Many follow-up studies have employed different methods to analyze human fMRI data with varying degrees of success [Nature reviews 7:7 (2006) 523-534].

Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information.

Local field potentials (LFPs) reflect subthreshold integrative processes that complement spike train measures. However, little is yet known about the differences between how LFPs and spikes encode rich naturalistic sensory stimuli. We addressed this question by recording LFPs and spikes from the primary visual cortex of anesthetized macaques while presenting a color movie.

Inferring spike trains from local field potentials.

We investigated whether it is possible to infer spike trains solely on the basis of the underlying local field potentials (LFPs). Using support vector machines and linear regression models, we found that in the primary visual cortex (V1) of monkeys, spikes can indeed be inferred from LFPs, at least with moderate success. Although there is a considerable degree of variation across electrodes, the low-frequency structure in spike trains (in the 100-ms range) can be inferred with reasonable accuracy, whereas exact spike positions are not reliably predicted.

Gene selection via the BAHSIC family of algorithms.

Motivation: Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics. The ultimate goal is to detect the genes that are involved in disease outbreak and progression. A multitude of methods have been proposed for this task of feature selection, yet the selected gene lists differ greatly between different methods.

Integrating structured biological data by Kernel Maximum Mean Discrepancy.

Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology.

The effect of artifacts on dependence measurement in fMRI.

The study of effective connectivity by means of neuroimaging depends on the measurement of similarity between activity patterns at different locations in the brain, without necessarily presupposing a particular model for this dependence. When these interactions are measured using functional magnetic resonance imaging (fMRI) techniques, however, imaging and physiological artifacts create patterns of dependence that may be unrelated to cortical activity. We demonstrate some of these effects through the measurement of short-range dependencies present in fMRI scans of the primary visual cortex (V1) in the anaesthetized macaque monkey.