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Séminaire Doctorants

organisé par l'équipe DOCT

  • Louise Martineau

    Topology and neuroscience : applying persistent homology to spike train analysis

    2 avril 2026 - 16:30Salle de conférences IRMA

    Persistent homology is a field that has emerged in data analysis over the past two decades. Its primary goal is to recover the topology of a point cloud. In this talk, we explore the applications of persistent homology to another type of data that is ubiquitous in neuroscience : spike trains. A spike train is a collection of binary time series corresponding to neuronal activity. We define the persistent homology of a spike train via a filtration based on the frequencies of cofiring neurons, and we explore the performance of this method with both simulated and real data.