Expressive architectures enhance interpretability of dynamics-based neural population models.

Sedler AR, Versteeg C, Pandarinath C

Open source

DOI
10.51628/001c.73987
Published
2023
Container
Neurons, behavior, data analysis, and theory
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.51628/001c.73987,
  title = {Expressive architectures enhance interpretability of dynamics-based neural population models.},
  author = {Sedler AR and Versteeg C and Pandarinath C},
  year = {2023},
  journal = {Neurons, behavior, data analysis, and theory},
  doi = {10.51628/001c.73987},
  url = {https://doi.org/10.51628/001c.73987}
}

RIS

TY  - JOUR
TI  - Expressive architectures enhance interpretability of dynamics-based neural population models.
AU  - Sedler AR
AU  - Versteeg C
AU  - Pandarinath C
PY  - 2023
JO  - Neurons, behavior, data analysis, and theory
DO  - 10.51628/001c.73987
UR  - https://doi.org/10.51628/001c.73987
ER  - 

APA

AR, S., C, V., & C, P. (2023). Expressive architectures enhance interpretability of dynamics-based neural population models.. Neurons, behavior, data analysis, and theory. https://doi.org/10.51628/001c.73987

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