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UID:20260720T220534EDT-6132lIjvKJ@132.216.98.100
DTSTAMP:20260721T020534Z
DESCRIPTION:Title: Model-free inference of network structural features from
  observed dynamics\n\nAbstract:The dynamics of biological networks enables
  the function of a variety of systems we rely on every day\, from gene and
  protein regulation to metabolic circuits and neural networks in the brain
 . Understanding and predicting network function relies on suitable models\
 , yet it remains unclear how to extract key features of networks if only t
 ime series data from (some) units are available. Here we report on recent 
 progress on model-free inference of network structural features from obser
 ved dynamics. First\, we demonstrate how to identify the number N of dynam
 ical variables making up a network -- arguably its most fundamental proper
 ty -- from recorded time series of only a small subset of n\n\n \n\n \n\nS
 eminar CAMBAM Seminar Series\n	En ligne/Web - https://mcgill.zoom.us/j/9158
 9192037\n\nWeb site : https://www.mcgill.ca/qls/seminars\n
DTSTART:20211005T160000Z
DTEND:20211005T170000Z
SUMMARY:Marc Timme (Dresden)
URL:https://www.mcgill.ca/mathstat/channels/event/marc-timme-dresden-333794
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