Hybrid neural networks in the mushroom body drive olfactory preference in Drosophila.

Saved in:
Bibliographic Details
Title: Hybrid neural networks in the mushroom body drive olfactory preference in Drosophila.
Authors: Cheng LS; Department of Physics, National Tsing Hua University, Hsinchu 300043, Taiwan., Charng CC; Institute of Systems Neuroscience and Department of Life Science, National Tsing Hua University, Hsinchu 30013, Taiwan., Chen RH; Institute of Systems Neuroscience and Department of Life Science, National Tsing Hua University, Hsinchu 30013, Taiwan., Feng KL; Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan., Chiang AS; Institute of Systems Neuroscience and Department of Life Science, National Tsing Hua University, Hsinchu 30013, Taiwan.; Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan.; Kavli Institute for Brain and Mind, University of California San Diego, La Jolla, CA 92093-0526, USA.; Institute of Physics, Academia Sinica, Taipei 11529, Taiwan.; Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80780, Taiwan.; Institute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli 35053, Taiwan.; Graduate Institute of Clinical Medical Science, China Medical University, Taichung 40402, Taiwan., Lo CC; Institute of Systems Neuroscience and Department of Life Science, National Tsing Hua University, Hsinchu 30013, Taiwan.; Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan., Lee TK; Department of Physics, National Tsing Hua University, Hsinchu 300043, Taiwan.; Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan.; Institute of Physics, Academia Sinica, Taipei 11529, Taiwan.
Source: Science advances [Sci Adv] 2025 May 30; Vol. 11 (22), pp. eadq9893. Date of Electronic Publication: 2025 May 30.
Publication Type: Journal Article
Journal Info: Publisher: American Association for the Advancement of Science Country of Publication: United States NLM ID: 101653440 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2375-2548 (Electronic) Linking ISSN: 23752548 NLM ISO Abbreviation: Sci Adv Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2375-2548
DOI:10.1126/sciadv.adq9893