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X-WR-CALDESC:Events for Biomedical Mathematics Group
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Seoul:20250509T140000
DTEND;TZID=Asia/Seoul:20250509T160000
DTSTAMP:20260423T100049
CREATED:20250426T142850Z
LAST-MODIFIED:20250507T002814Z
UID:11058-1746799200-1746806400@www.ibs.re.kr
SUMMARY:Network inference from short\, noisy\, low time-resolution\, partial measurements: Application to C. elegans neuronal calcium dynamics - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper “Network inference from short\, noisy\, low time-resolution\, partial measurements: Application to C. elegans neuronal calcium dynamics” by Amitava Banerjee\, Sarthak Chandra\, and Edward Ott\, PNAS\, 2023. \nAbstract \nNetwork link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications\, e.g.\, estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases\, the measured time series data may be subject to limitations\, including limited duration\, low sampling rate\, observational noise\, and partial nodal state measurement. However\, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here\, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality\, transfer entropy\, and\, a machine learning-based method. Furthermore\, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.
URL:https://www.ibs.re.kr/bimag/event/chaos-is-not-rare-in-natural-ecosystems-olive-cawiding/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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