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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Seoul:20250314T110000
DTEND;TZID=Asia/Seoul:20250314T120000
DTSTAMP:20260521T231353
CREATED:20250217T075146Z
LAST-MODIFIED:20250217T075146Z
UID:10749-1741950000-1741953600@www.ibs.re.kr
SUMMARY:COVID-19 and Challenges to the Classical Theory of Epidemics - Simon Levin
DESCRIPTION:Abstract \nThe standard theory of infectious diseases\, tracing back to the work of Kermack and McKendrick nearly a century ago\, has been a triumph of mathematical biology\, a rare marriage of theory and application. Yet the limitations of its most simple representations\, which has always been known\, have been laid bare in dealing with COVID-19\, sparking a spate of extensions of the basic theory to deal more effectively with aspects of viral evolution\, asymptotic stages\, heterogeneity of various kinds\, the ambiguities of notions of herd immunity\, the role of social behaviors and other features. This lecture will address some progress in addressing these\, and open challenges in expanding the mathematical theory.
URL:https://www.ibs.re.kr/bimag/event/covid-19-and-challenges-to-the-classical-theory-of-epidemics-simon-levin/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/02/simon-levin-e1739778689468.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250314T140000
DTEND;TZID=Asia/Seoul:20250314T160000
DTSTAMP:20260521T231353
CREATED:20250226T070011Z
LAST-MODIFIED:20250226T070011Z
UID:10806-1741960800-1741968000@www.ibs.re.kr
SUMMARY:A biological model of nonlinear dimensionality reduction - Shingo Gibo
DESCRIPTION:In this talk\, we discuss the paper “A biological model of nonlinear dimensionality reduction” by K. Yoshida and T. Toyoizumi\, Science Advances\, 2025\, at the Journal Club. \nAbstract \nObtaining appropriate low-dimensional representations from high-dimensional sensory inputs in an unsupervised manner is essential for straightforward downstream processing. Although nonlinear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) have been developed\, their implementation in simple biological circuits remains unclear. Here\, we develop a biologically plausible dimensionality reduction algorithm compatible with t-SNE\, which uses a simple three-layer feedforward network mimicking the Drosophila olfactory circuit. The proposed learning rule\, described as three-factor Hebbian plasticity\, is effective for datasets such as entangled rings and MNIST\, comparable to t-SNE. We further show that the algorithm could be working in olfactory circuits in Drosophila by analyzing the multiple experimental data in previous studies. We lastly suggest that the algorithm is also beneficial for association learning between inputs and rewards\, allowing the generalization of these associations to other inputs not yet associated with rewards.
URL:https://www.ibs.re.kr/bimag/event/a-biological-model-of-nonlinear-dimensionality-reduction-shingo-gibo/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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