Switching state-space modeling of neural signal dynamics
Fig 4
Simulation results: Example segmentation on data generated from a different switching model class when true parameters are known.
The top two panels show the two sequences, y1 and y2, recorded as a bivariate observation y. Sequence y2 has a non-zero influence on sequence y1 as shown by the upward arrows, according to a switching state st. The time traces are also marked with different colors for the two switching states. The bottom panel shows the results of switching inference on the example data given true parameters. Time points estimated to be in the first model (st = 1) are marked in colored dots for each inference method, with accuracy shown in parentheses. True = ground truth; Random = random segmentation with a Bernoulli process; Static = static switching method; IMM = interacting multiple models method; VI-A = variational inference with deterministic annealing (orange color); VI-I = variational inference with interpolated densities (blue color).