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Michale S. Fee

Affiliations: 
Massachusetts Institute of Technology, Cambridge, MA, United States 
Area:
bird song
Website:
http://web.mit.edu/feelab/
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"Michale Fee"
Mean distance: 12.88 (cluster 6)
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Parents

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Steven Chu grad student 1988-1992 Stanford (Physics Tree)
David Kleinfeld post-doc Bell Labs

Children

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Timothy A. Currier research assistant MIT
Anthony Leonardo grad student Bell Labs
J R Scherrer grad student
Michael Stetner grad student 2010- MIT
Galen Forest Lynch grad student 2011- MIT
Emily L. Mackevicius grad student 2011- MIT
Andrew H. Bahle grad student 2017- MIT
Aditya Nair grad student 2019- MIT
Aaron Samuel Andalman grad student 2003-2009 MIT
Dmitriy Aronov grad student 2005-2010 MIT
Lena Veit grad student 2009-2010 MIT
Tatsuo Okubo grad student 2008-2015 MIT
Jesse H. Goldberg post-doc MIT
Richard H.R. Hahnloser post-doc Bell Labs
Joergen Kornfeld post-doc
Alexay Kozhevnikov post-doc Bell Labs
Anusha Narayan post-doc McGovern Instittute for Brain Research, MIT
Nader Nikbakht post-doc MIT
Bence P. Olveczky post-doc MIT
Timothy J. Gardner post-doc 2005-2008 MIT
Michael A. Long post-doc 2003-2009 MIT
Liora Las post-doc 2006-2011 MIT

Collaborators

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Winfried Denk collaborator Bell Labs
Ila R. Fiete collaborator MIT
Dezhe Jin collaborator 2008- MIT
Richard D. Mooney collaborator 2004-2006 MIT
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Publications

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Mackevicius EL, Gu S, Denisenko NI, et al. (2023) Self-organization of songbird neural sequences during social isolation. Elife. 12
Scherrer JR, Lynch GF, Zhang JJ, et al. (2023) An optical design enabling lightweight and large field-of-view head-mounted microscopes. Nature Methods
Schubert PJ, Dorkenwald S, Januszewski M, et al. (2022) SyConn2: dense synaptic connectivity inference for volume electron microscopy. Nature Methods. 19: 1367-1370
Mackevicius EL, Happ MTL, Fee MS. (2020) An avian cortical circuit for chunking tutor song syllables into simple vocal-motor units. Nature Communications. 11: 5029
Mackevicius EL, Bahle AH, Williams AH, et al. (2019) Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience. Elife. 8
Mackevicius EL, Bahle AH, Williams AH, et al. (2018) Author response: Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience Elife
Mackevicius EL, Fee MS. (2017) Building a state space for song learning. Current Opinion in Neurobiology. 49: 59-68
Danish HH, Aronov D, Fee MS. (2017) Rhythmic syllable-related activity in a songbird motor thalamic nucleus necessary for learned vocalizations. Plos One. 12: e0169568
Lynch GF, Okubo TS, Hanuschkin A, et al. (2016) Rhythmic Continuous-Time Coding in the Songbird Analog of Vocal Motor Cortex. Neuron. 90: 877-92
Okubo TS, Mackevicius EL, Payne HL, et al. (2015) Growth and splitting of neural sequences in songbird vocal development. Nature
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