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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210507T123000
DTEND;TZID=Asia/Seoul:20210507T133000
DTSTAMP:20260427T193247
CREATED:20210503T075749Z
LAST-MODIFIED:20210503T075749Z
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SUMMARY:Introduction to Bayesian ML/DL\, with Application to Parameter Inference of Coupled Non-linear ODEs - Part 2
DESCRIPTION:In this talk\, the speaker will present introductory materials about Bayesian Machine Learning. \nAbstract\nThe problem of approximating the posterior distribution (or density estimation in general) is a crucial problem in Bayesian statistics\, in which intractable integrals often become the computational bottleneck. MCMC sampling is the most widely used family of algorithms for approximating posteriors. However\, if the underlying graphical model is too complex or the data is in very high dimensions\, then such sampling-based methodologies run into several problems. Variational inference (Jordan et al.\, 1999; Wainwright and Jordan\, 2008) is a family of machine learning methodologies that transforms the problem of approximating posterior densities to an optimization\, which lets us circumvent all such problems. In the first part\, I will introduce the general framework of variational inference and some underlying theory\, accompanied by an illustrative example of LDA (Blei et al.\, 2003). In the second part\, I will introduce some recent works on applying variational inference to parameter inference of coupled non-linear ODEs arising in various biological contexts.
URL:https://www.ibs.re.kr/bimag/event/introduction-to-bayesian-ml-dl-with-application-to-parameter-inference-of-coupled-non-linear-odes-part-2/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210514T110000
DTEND;TZID=Asia/Seoul:20210514T120000
DTSTAMP:20260427T193247
CREATED:20210507T124508Z
LAST-MODIFIED:20210507T124508Z
UID:4555-1620990000-1620993600@www.ibs.re.kr
SUMMARY:Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model
DESCRIPTION:We will discuss about “Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model”\, Ito et. al.\, PloS ONE\, 2011 \nTransfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive\, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic\, as synaptic delays between cortical neurons\, for example\, range from one to tens of milliseconds. In addition\, neurons produce bursts of spikes spanning multiple time bins. To address these issues\, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance\, we applied these extensions of TE to a spiking cortical network model (Izhikevich\, 2006) with known connectivity and a range of synaptic delays. For comparison\, we also investigated single-delay TE\, at a message length of one bin (D1TE)\, and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE\, this dramatically improved to 73% of true connections. In addition\, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE\, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE\, when used on currently available desktop computers\, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.
URL:https://www.ibs.re.kr/bimag/event/2021-05-14/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210520T123000
DTEND;TZID=Asia/Seoul:20210520T133000
DTSTAMP:20260427T193247
CREATED:20210507T123654Z
LAST-MODIFIED:20210507T123746Z
UID:4547-1621513800-1621517400@www.ibs.re.kr
SUMMARY:Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes
DESCRIPTION:We will discuss about “Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes”\, Hempel et. al.\, bioRxiv\, 2021 \nIn order to advance the mission of in silico cell biology\, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) and Markov state models (MSMs) have enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success\, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes\, the number of independent or weakly coupled subsystems increases\, and the number of global system states increase exponentially\, making the sampling of all distinct global states unfeasible. In this work\, we present a technique called Independent Markov Decomposition (IMD) that leverages weak coupling between subsystems in order to compute a global kinetic model without requiring to sample all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology\, we demonstrate that IMD can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.
URL:https://www.ibs.re.kr/bimag/event/2021-05-20/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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