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SUMMARY:BMI Progress Reports 2021 // Prof. Hess Bellwald's Lab: S. Ebli & 
 C. Hacker\, Topological methods in neuroscience
DTSTART:20210310T121500
DTEND:20210310T130000
DTSTAMP:20260510T135141Z
UID:6de19e76ab31cf80fdf8c5776dcd4804652465f112a13ac85c8b6ef4
CATEGORIES:Conferences - Seminars
DESCRIPTION:S. Ebli & C. Hacker\nHistorically topology studies shapes up t
 o deformation\, providing insight into the global structure of geometric o
 bjects. Recently\, topological methods have been successfully applied to a
 nalyze high dimensional data in various settings in biology.\n\nIn this ta
 lk we will present two topological methods that can be applied to neurosci
 ence data.\n\nThe first method applies to the analysis of connectomes. It 
 is a generalization of the node2vec algorithm\, which has previously been 
 used to study structural and functional connectomes. It has been shown tha
 t analyzing brain networks through the lens of higher dimensional structur
 es\, called simplices\, can be more informative than using just the struct
 ure given by pairwise interactions. To this end\, we suggest a method that
  aims at understanding higher dimensional structures in networks by obtain
 ing a vectorized representation of these structures\, in a similar way to 
 node2vec.\n\nThe second method applies to a class of recurrent neural netw
 orks used for modelling neural activity\, called combinatorial threshold-l
 inear networks (CTLNs). The dynamics of these networks is determined by th
 e structure of a directed graph. We propose a topologically inspired metho
 d to reduce the size of the dynamical system.  Moreover\, we will see how
  from the reduced dynamical system we can predict properties of the dynami
 cs in the original network.\n 
LOCATION:Online
STATUS:CONFIRMED
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