Machine-learning approaches to classify and understand emotion states in mice

Dolensek, Nejc; Gogolla, Nadine

doi:10.1038/s41386-020-00857-8

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Machine-learning approaches to classify and understand emotion states in mice

Dolensek, N., & Gogolla, N. (2021). Machine-learning approaches to classify and understand emotion states in mice. Neuropsychopharmacology, 46(1), 250-251. doi:10.1038/s41386-020-00857-8.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000A-7A24-5 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-7A25-4

Genre: Journal Article

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Creators:
Dolensek, Nejc¹, Author
Gogolla, Nadine¹, Author

Affiliations:
1Max Planck Research Group: Circuits for Emotion / Gogolla, MPI of Neurobiology, Max Planck Society, ou_2054290

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Free keywords: FACIAL EXPRESSIONSNeurosciences & Neurology; Pharmacology & Pharmacy; Psychiatry;

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Language(s): eng - English

Dates: Date issued: 2021-01-01

Publication Status: Issued

Pages: 2

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Table of Contents: -

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Identifiers: ISI: 000567377000001
DOI: 10.1038/s41386-020-00857-8

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Project name : European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (ERC2017-STG, grant agreement no. 758448 to N.G.)

Grant ID : 758448

Funding program : Horizon 2020 (H2020)

Funding organization : European Commission (EC)

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Title: Neuropsychopharmacology

Other : Neuropsychopharmacology: off. publ. of the American College of Neuropsychopharmacology

Abbreviation : Neuropsychopharmacol

Source Genre: Journal

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Publ. Info: New York, NY : Springer Nature

Pages: - Volume / Issue: 46 (1) Sequence Number: - Start / End Page: 250 - 251 Identifier: ISSN: 0893-133X
CoNE: https://pure.mpg.de/cone/journals/resource/954925558485