"No sufro, estoy bien/I am not suffering, so I am doing OK": A mixed method exploration of individual and network-level factors and Type 2 Diabetes Mellitus (T2DM) among Mexican American adults in New York City.

Introduction: The prevalence of type 2 Diabetes Mellitus (T2DM) is 2-3 times greater among Mexican Americans than non-Latino whites, and Mexican Americans are more likely to develop T2DM at younger ages and experience higher rates of complications. Social networks might play a crucial role in both T2DM etiology and management through social support, access to resources, social engagement, and health behavioral norms.

Objective: To quantitatively identify the social network features associated with glycated hemoglobin (HbA1c) in a community sample of Mexican immigrants residing in New York City, and to explore the extent to which these quantitative findings converge with qualitative narratives of their lived experiences.

Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods.

Estimating two-dimensional firing rate maps is a common problem, arising in a number of contexts: the estimation of place fields in hippocampus, the analysis of temporally nonstationary tuning curves in sensory and motor areas, the estimation of firing rates following spike-triggered covariance analyses, etc. Here we introduce methods based on Gaussian process nonparametric Bayesian techniques for estimating these two-dimensional rate maps. These techniques offer a number of advantages: the estimates may be computed efficiently, come equipped with natural errorbars, adapt their smoothness automatically to the local density and informativeness of the observed data, and permit direct fitting of the model hyperparameters (e.

Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness.

There has recently been a great deal of interest in inferring network connectivity from the spike trains in populations of neurons. One class of useful models that can be fit easily to spiking data is based on generalized linear point process models from statistics. Once the parameters for these models are fit, the analyst is left with a nonlinear spiking network model with delays, which in general may be very difficult to understand analytically.

A new look at state-space models for neural data.

State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the approximate methods.