Byron Yu

Yu Group at Carnegie Mellon University. Neuroscience, brain-computer interfaces, machine learning

[Byron Yu @YuLikeNeuro]

If you have a background in neural technologies or related disciplines, and are enthusiastic about teaching, we encourage you to apply here:

https://t.co/kKN0H6nhn9

[Byron Yu @YuLikeNeuro]

If you have a background in neural technologies or related disciplines, and are enthusiastic about teaching, we encourage you to apply here:

https://t.co/kKN0H6nhn9

[Byron Yu @YuLikeNeuro]

We (CMU Biomedical Engineering and Neuroscience Institute) are seeking to hire a new teaching-track faculty member for our new joint MS program in Neural Technologies (MiNT).

[Byron Yu @YuLikeNeuro]

@MarkChurchland @mjaz_jazlab @FieteGroup @BrunoAverbeck Thank you to @neuroamyo for a wonderful News & Views piece:

https://t.co/LLDMNdnx7Z

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[Byron Yu @YuLikeNeuro]

We are excited to share our work on dynamical constraints on neural population activity, published as a cover article in @NatureNeuro. It was led by Emily Oby, @AlanDegenhart, @ErinnGrigsby, with @aaronbatista and team.

https://t.co/Oi2wXl0MIz

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[Byron Yu @YuLikeNeuro]

@MarkChurchland @mjaz_jazlab @FieteGroup @BrunoAverbeck Here is the cover image (credit Avesta Rastan):

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[Scott Linderman @scott_linderman]

📣 The 11th Statistical Analysis of Neuronal Data (SAND11) workshop will be held June 11-13, 2025 in New York at the @FlatironInst with generous support from @SimonsFdn!

Submissions for talks+posters are due March 21. See our website for details 👇

https://t.co/9P7WhHq6v4

[Byron Yu @YuLikeNeuro]

We've posted a new paper about how to speed up statistical methods for analyzing multi-area recordings by orders of magnitude:

https://t.co/SZ9zLyHS6C

This work was led by Evren Gokcen, with Anna Jasper (Einstein), Adam Kohn (Einstein), and Christian Machens (Champalimaud).

[CMU College of Engineering @CMUEngineering]

Transformative work happens through collaboration. New research from @CMUEngineering & @PittEngineering validates principles that researchers have brought out in neural network models for decades. @YuLikeNeuro @cmu_bme

https://t.co/I1l2cszLJ5

[Nature Neuroscience @NatureNeuro]

Using a brain–computer interface to challenge monkeys to override their natural time courses of neural activity reveals that the time courses are highly robust, suggestive of network-level computational mechanisms

@aaronbatista @YuLikeNeuro @AlanDegenhart @ErinnGrigsb

https://t.co/DeQJdmVzc9

[Yuki Minai @curi_ms]

I'm excited to share our #NeurIPS2024 paper with
@jsoldadomagrane @SmithLabNeuro @YuLikeNeuro!
We develop a new brain stimulation framework (MiSO) to drive neural population activity toward specified states.
Paper: https://t.co/1VHPSHDu8d
Poster Session 4 East, Dec 12 16:30
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[Byron Yu @YuLikeNeuro]

We encourage you to try SNOPS here:

https://t.co/nIQWYxO5Dm

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[Byron Yu @YuLikeNeuro]

We are excited to share our new method, SNOPS (Spiking Network Optimization using Population Statistics), published in
@NatComputSci. It was led by @Shenghao_W, with @cc_huang11, Adam Snyder, @SmithLabNeuro, and @BrentDoiron.

https://t.co/Wnn7Ufp3k0

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[Byron Yu @YuLikeNeuro]

SNOPS can also identify specific combinations of activity statistics that the model cannot reproduce, thereby guiding model development.

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