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BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS
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 noisyExpand
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Linear dynamical neural population models through nonlinear embeddings
TLDR
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
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Spectral methods for neural characterization using generalized quadratic models
TLDR
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
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Bayesian entropy estimation for countable discrete distributions
TLDR
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
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Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data
TLDR
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
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Low-dimensional models of neural population activity in sensory cortical circuits
TLDR
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
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Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
TLDR
We introduce a flexible algorithmic framework for fast, efficient and accurate extraction of neural spikes from imaging data. Expand
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Universal models for binary spike patterns using centered Dirichlet processes
TLDR
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
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Bayesian estimation of discrete entropy with mixtures of stick-breaking priors
TLDR
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
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Bayesian entropy estimation for binary spike train data using parametric prior knowledge
TLDR
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
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