학술논문
Most discriminative stimuli for functional cell type clustering.
Document Type
Author
Burg MF; International Max Planck Research School for Intelligent Systems, Tübingen, Germany.; Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany.; Tübingen AI Center, University of Tübingen, Germany.; Zenkel T; Institute of Ophthalmic Research, University of Tübingen, Germany.; Centre for Integrative Neuroscience, University of Tübingen, Germany.; Vystrčilová M; Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany.; Oesterle J; Institute of Ophthalmic Research, University of Tübingen, Germany.; Centre for Integrative Neuroscience, University of Tübingen, Germany.; Höfling L; Institute of Ophthalmic Research, University of Tübingen, Germany.; Centre for Integrative Neuroscience, University of Tübingen, Germany.; Willeke KF; International Max Planck Research School for Intelligent Systems, Tübingen, Germany.; Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany.; Institute for Bioinformatics and Medical Informatics, Tübingen University, Germany.; Lause J; Tübingen AI Center, University of Tübingen, Germany.; Hertie Institute for AI in Brain Health, University of Tübingen, Germany.; Müller S; International Max Planck Research School for Intelligent Systems, Tübingen, Germany.; Tübingen AI Center, University of Tübingen, Germany.; Hertie Institute for AI in Brain Health, University of Tübingen, Germany.; Fahey PG; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.; Ding Z; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.; Restivo K; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.; Sridhar S; University Medical Center Göttingen, Department of Ophthalmology, Germany.; Bernstein Center for Computational Neuroscience Göttingen, Germany.; Gollisch T; University Medical Center Göttingen, Department of Ophthalmology, Germany.; Bernstein Center for Computational Neuroscience Göttingen, Germany.; Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC), University of Göttingen, Germany.; Berens P; Tübingen AI Center, University of Tübingen, Germany.; Hertie Institute for AI in Brain Health, University of Tübingen, Germany.; Tolias AS; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.; Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.; Euler T; Institute of Ophthalmic Research, University of Tübingen, Germany.; Centre for Integrative Neuroscience, University of Tübingen, Germany.; Bethge M; Tübingen AI Center, University of Tübingen, Germany.; Centre for Integrative Neuroscience, University of Tübingen, Germany.; Ecker AS; Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany.; Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.
Source
Country of Publication: United States NLM ID: 101759493 Publication Model: Electronic Cited Medium: Internet ISSN: 2331-8422 (Electronic) Linking ISSN: 23318422 NLM ISO Abbreviation: ArXiv Subsets: PubMed not MEDLINE
Subject
Language
English
Abstract
Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron.