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DTSTART:20250330T030000
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DTSTAMP:20260424T143335Z
UID:664da6bce61b0428004584@ist.ac.at
DTSTART:20250207T150000
DTEND:20250207T160000
DESCRIPTION:Speaker: Alexis Benichou\nAbstract: Physical and functional con
 straints on biological networks lead to structural patterns across multipl
 e scales in their organization. A particular type of higher-order network 
 feature that has received considerable interest is network motifs\, define
 d as statistically regular subgraphs. These may implement fundamental logi
 cal and computational circuits and are referred to as building blocks of c
 omplex networks. Their well-defined structures and small sizes also enable
  the testing of their functions in synthetic and natural biological experi
 ments.I will present an inference framework for motif mining based on loss
 less network compression. This provides an alternative definition of motif
  significance which allows comparing different motifs and selecting the co
 llectively most significant set of motifs as well as other prominent netwo
 rk features in terms of their combined compression of the network. This ap
 proach overcomes common problems in hypothesis testing-based motif analysi
 s and guarantees robust statistical inference. I will show how this applie
 s to neural wiring diagrams\, termed connectomes\, including the nematode 
 Caenorhabditis elegans and the fruit fly Drosophila melanogaster at differ
 ent developmental stages.
LOCATION:Mondi Seminar Room 2\, Central Building\, ISTA
ORGANIZER:aehrmann@ist.ac.at
SUMMARY:Alexis Benichou: Statistical inference of circuit motifs in biologi
 cal neural networks
URL:https://talks-calendar.ista.ac.at/events/5007
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