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We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on data sets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https:\/\/github.com\/ttesileanu\/bio-time-series.<\/jats:p>","DOI":"10.1162\/neco_a_01476","type":"journal-article","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T15:23:11Z","timestamp":1642087391000},"page":"891-938","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":3,"title":["Neural Circuits for Dynamics-Based Segmentation of Time Series"],"prefix":"10.1162","volume":"34","author":[{"given":"Tiberiu","family":"Te\u015fileanu","sequence":"first","affiliation":[{"name":"Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, U.S.A. ttesileanu@gmail.com"}]},{"given":"Siavash","family":"Golkar","sequence":"additional","affiliation":[{"name":"Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, U.S.A. sgolkar@flatironinstitute.org"}]},{"given":"Samaneh","family":"Nasiri","sequence":"additional","affiliation":[{"name":"Department of Neurology, Harvard Medical School, Boston, MA 02115, U.S.A. samaneh.nasiri.gh@gmail.com"}]},{"given":"Anirvan M.","family":"Sengupta","sequence":"additional","affiliation":[{"name":"Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010"},{"name":"Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854, U.S.A. anirvans@physics.rutgers.edu"}]},{"given":"Dmitri B.","family":"Chklovskii","sequence":"additional","affiliation":[{"name":"Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010"},{"name":"Neuroscience Institute, NYU Langone Medical Center, New York, NY, U.S.A. dchklovskii@flatironinstitute.org"}]}],"member":"281","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"issue":"19","key":"2022032817111047100_B1","doi-asserted-by":"publisher","first-page":"8616","DOI":"10.1073\/pnas.92.19.8616","article-title":"Cortical activity flips among quasi-stationary states","volume":"92","author":"Abeles","year":"1995","journal-title":"PNAS"},{"key":"2022032817111047100_B2","unstructured":"Adams, R. 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