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The brain must extract behaviorally relevant latent variables from thesignals streamed by the sensory organs. Such latent variables are often encodedin the dynamics that generated the signal rather than in the specificrealization of the waveform. Therefore, one problem faced by the brain is tosegment time series based on underlying dynamics. We present two algorithms forperforming this segmentation task that are biologically plausible, which wedefine as acting in a streaming setting and all learning rules being local. Onealgorithm is model-based and can be derived from an optimization probleminvolving a mixture of autoregressive processes. This algorithm relies onfeedback in the form of a prediction error, and can also be used forforecasting future samples. In some brain regions, such as the retina, thefeedback connections necessary to use the prediction error for learning areabsent. For this case, we propose a second, model-free algorithm that uses arunning estimate of the autocorrelation structure of the signal to perform thesegmentation. We show that both algorithms do well when tasked with segmentingsignals drawn from autoregressive models with piecewise-constant parameters. Inparticular, the segmentation accuracy is similar to that obtained fromoracle-like methods in which the ground-truth parameters of the autoregressivemodels are known. We also test our methods on datasets generated by alternatingsnippets of voice recordings. We provide implementations of our algorithms athttps://github.com/ttesileanu/bio-time-series.

Neural circuits for dynamics-based segmentation of time series