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SUMMARY:SDSC-AI4Science seminar: Modeling the collective dynamics of in-vi
 tro populations of neurons
DTSTART:20221116T160000
DTEND:20221116T170000
DTSTAMP:20260527T232020Z
UID:3ced49fa44a539f7ca7c467087a64c095b584e61039c657e4752b1f2
CATEGORIES:Conferences - Seminars
DESCRIPTION:Christian Donner\nWe are happy to announce the SDSC - AI4Scien
 ce monthly seminar\, a seminar co-organized by the EPFL AI4Science Initiat
 ive and the Swiss Data Science Center and focussing on projects in which d
 ata science\, statistics\, machine learning and AI are applied to the scie
 nces. Each seminar will feature a presentation of one applied project\, ge
 ared towards an audience with expertise in Data Science methods\, from the
  initial formulation of a research question in science associated with sou
 rces of data\, to the model\, algorithms and analyses produced. The presen
 tation will be highlighting the choices made\, the challenges encountered\
 , interesting technical questions and possible further developments. A num
 ber of the projects presented will be collaborative projects of the Swiss 
 Data Science Center. One of the objectives of the seminar is to foster exc
 hanges between researchers working in methods and applied data science res
 earch in the sciences\, and to create new opportunities of collaborations.
 \n\nEach session will feature a talk followed by a discussion with questio
 ns from the audience.\n\nThe first seminar will take place on November 16t
 h at 16h00 in CM 1 3.\n\nSpeaker: Christian Donner\, Senior Data Scientist
  at SDSC\n\nTitle: Modeling the collective dynamics of in-vitro population
 s of neurons\n\nAbstract: In the DeepEphys project we aim at improving our
  understanding of the neurophysiological basis of Parkinson’s disease (P
 D). To this end\, we recorded in-vitro activity from healthy and PD neuron
 al cultures that have been derived from induced pluripotent stem cells. Th
 e talk’s focus will be on how we can use such large-scale recordings to 
 characterise the collective neuronal behaviour. I will present two approac
 hes that we followed: First\, using discriminative classification models w
 e identify distinctive physiologically meaningful predictors for PD. In th
 e second part\, switching gears\, we focus on learning a generative proces
 s of the observed data. More precisely\, we will consider a Cox process mo
 del for the observed neuronal activity\, i.e. a doubly stochastic point pr
 ocess\, where the neurons’ firing rate is modelled nonparametrically by 
 a Gaussian process. The Gaussian process is defined over a space of the pa
 st neuronal activity. Once the model is learned via variational inference\
 , we can derive a set of differential equations for the neuronal populatio
 n\, which allows us to use tools from dynamical systems analysis to gain i
 nsights into the dynamics of the underlying neuronal culture. This way we 
 can formulate experimentally testable hypotheses for the future.
LOCATION:CM 1 3 https://plan.epfl.ch/?room==CM%201%203
STATUS:CONFIRMED
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