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Switching state-space modeling of neural signal dynamics

Fig 9

Generative structure with parallel switching state-space models.

A directed acyclic graph is shown to represent the conditional independence structure between the M real-valued Gaussian hidden state sequences for m ∈ {1, ⋯ M}, a discrete-valued hidden Markov chain {s_t}, and the observed data {y_t} up to time T. In this generative structure, the observation at a given time point depends only on the hidden states of the M Gaussian models at that point, with the discrete-valued state selecting one of the models to produce the observation, hence the name switching.

doi: https://doi.org/10.1371/journal.pcbi.1011395.g009