Highlights•New generations of recording and computing technologies have enabled
neuroscience at the level of the neural population.•Landmark scientific findings suggest the …

Until now, most brain studies have focused on small numbers of neurons that interact in
limited circuits, allowing analysis of individual computations or steps of neural processing …

Achieving state-of-the-art performance with deep neural population dynamics models
requires extensive hyperparameter tuning for each dataset. AutoLFADS is a model-tuning …

Modern recording techniques enable large-scale measurements of neural activity in a
variety of model organisms. The dynamics of neural activity shed light on how organisms …

Highlights•Latent variable (LV) models are often used to visualize neural population
activity.•New analyses must go beyond visualization and relate explicitly to …

Modern neural interfaces allow access to the activity of up to a million neurons within brain
circuits. However, bandwidth limits often create a trade-off between greater spatial sampling …

Abstract Sequential Monte Carlo (SMC) and Variational Inference (VI) are two families of
approximate inference algorithms for Bayesian latent variable models. A body of recent work …

Dynamical systems are the governing force behind many real world phenomena and
temporally correlated data. Recently, a number of neural network architectures have been …

We introduce graph gamma process (GGP) linear dynamical systems to model real-valued
multivariate time series. For temporal pattern discovery, the latent representation under the …

Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetic Inference Page 1
Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetic Inference Antonio …