Author

Christian Donner

ETH ZurichORCID 0000-0002-4499-2895Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:electronic health records· 3 Research Focus:benchmarking· 2 Research Focus:cell type annotation· 2 Approach:statistical model· 2 Research Focus:trajectory inference· 2 Research Focus:clustering· 1 Approach:deep learning· 1 Organism:human· 1

Frequent Co-Authors

Andreas R. HierlemanniD· 2 shared Damian RoqueiroiD· 2 shared Manuel S. SchröteriD· 2 shared Philipp HornaueriD· 2 shared Taehoon KimiD· 2 shared Fiscella Michele· 1 shared Guillaume ObozinskiiD· 1 shared Gustavo Prack· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 32 mutual co-author links

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2 shared papersNeuroscience

Andreas R. HierlemannORCID

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3 papers

Recent·Most cited

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Christian Donner, Julian Bartram, Philipp Hornauer, Taehoon Kim, Damian Roqueiro, Andreas R. Hierlemann, Guillaume Obozinski, Manuel S. Schröter

This paper introduces an ensemble artificial neural network (eANN) to improve neuronal connectivity inference from spike trains, showing it outperforms existing methods across different dynamical regimes.

PLoS Computational Biology · Apr 29, 2024Peer-reviewedNeuroscience

Downregulating α-synuclein in iPSC-derived dopaminergic neurons mimics electrophysiological phenotype of the A53T mutation

Philipp Hornauer, Gustavo Prack, Nadia Anastasi, Silvia Ronchi, Taehoon Kim, Christian Donner, Fiscella Michele, Karsten Michael Borgwardt, Verdon Taylor, Ravi Jagasia, Damian Roqueiro, Andreas R. Hierlemann, Manuel S. Schröter

The paper introduces DeePhys, a tool for functional phenotyping of neurons using electrophysiological data, and shows it can predict genotype and age in iPSC-derived DA neurons with the A53T SNCA mutation.

bioRxiv · Mar 31, 2022 · PreprintNeuroscience

Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models

Christian Donner, Manfred Opper, Josef Ladenbauer

The paper introduces a mechanistic model to infer population-level neuronal dynamics from single-trial spike trains, showing improved accuracy over phenomenological models.

bioRxiv · Jun 14, 2019 · PreprintNeuroscience