Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure both from noisy… Expand

We propose fLDS, a general class of nonlinear generative models that permits the firing rate of each neuron to vary as an arbitrary smooth function of a latent, linear dynamical state.Expand

We describe a set of fast, tractable methods for characterizing neural responses to high-dimensional sensory stimuli using a model we refer to as the generalized quadratic model (GQM).Expand

We consider the problem of estimating Shannon's entropy H from discrete data, in cases where the number of possible symbols is unknown or even countably infinite.Expand

We propose ÎMOD, a novel Bayesian mutual information estimator using a mixture-ofDirichlets prior, with mixing weights designed to produce an approximately flat prior over MI.Expand

We introduce a statistical model of neural population activity that integrates a nonlinear receptive field model with a latent dynamical model of ongoing cortical activity that captures temporal dynamics and correlations due to shared stimulus drive.Expand

We propose a family of "universal" models for binary spike patterns, where universality refers to the ability to model arbitrary distributions over all 2m binary patterns.Expand

We consider the problem of estimating Shannon's entropy H in the under-sampled regime, where the number of possible symbols may be unknown or countably infinite.Expand

We develop Bayesian estimators for the entropy of binary spike trains using priors designed to flexibly exploit the statistical structure of simultaneously-recorded spike responses.Expand