{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T14:25:20Z","timestamp":1759587920329,"version":"3.37.0"},"reference-count":74,"publisher":"MIT Press","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2009,7]]},"abstract":"<jats:p>UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.<\/jats:p>","DOI":"10.1162\/neco.2009.06-08-799","type":"journal-article","created":{"date-parts":[[2009,3,27]],"date-time":"2009-03-27T00:44:59Z","timestamp":1238114699000},"page":"1797-1862","source":"Crossref","is-referenced-by-count":36,"title":["Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States"],"prefix":"10.1162","volume":"21","author":[{"given":"Zhe","family":"Chen","sequence":"first","affiliation":[{"name":"Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, U.S.A., and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sujith","family":"Vijayan","sequence":"additional","affiliation":[{"name":"Program in Neuroscience, Harvard University, Cambridge, MA 02139, U.S.A., and Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riccardo","family":"Barbieri","sequence":"additional","affiliation":[{"name":"Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew A.","family":"Wilson","sequence":"additional","affiliation":[{"name":"Picower Institute for Learning and Memory, RIKEN-MIT Neuroscience Research Center, Department of Brain and Cognitive Sciences and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emery N.","family":"Brown","sequence":"additional","affiliation":[{"name":"Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, U.S.A., Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, U.S.A., and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.92.19.8616"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/4\/3\/018"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.2307\/2532392"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1999.0432"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1979.10489756"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0270(00)00344-7"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1101\/lm.73504"},{"key":"B8","first-page":"1","volume":"3","author":"Baum L.","year":"1972","journal-title":"Inequalities"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177697196"},{"volume-title":"Dynamic programming","year":"1957","author":"Bellman R.","key":"B10"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1007\/BF00318010"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.03711"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/S0924-8099(05)80020-4"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1201\/9780203494462.ch9"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1162\/08997660252741149"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1038\/nn1228"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780195301069.001.0001"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.2307\/2291149"},{"key":"B20","first-page":"1949","volume-title":"Proc. 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