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Found 5 papers, 3 papers with code
Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model
The Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) has been used widely as a natural Bayesian nonparametric extension of the classical Hidden Markov Model for learning from sequential and time-series data.
Maximum Entropy Flow Networks
Maximum entropy modeling is a flexible and popular framework for formulating statistical models given partial knowledge.
Linear dynamical neural population models through nonlinear embeddings
A body of recent work in modeling neural activity focuses on recovering low-dimensional latent features that capture the statistical structure of large-scale neural populations.
High-dimensional neural spike train analysis with generalized count linear dynamical systems
Latent factor models have been widely used to analyze simultaneous recordings of spike trains from large, heterogeneous neural populations.
A structured matrix factorization framework for large scale calcium imaging data analysis
We present a structured matrix factorization approach to analyzing calcium imaging recordings of large neuronal ensembles.
Neurons and Cognition Quantitative Methods Applications
