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Creating 10.21105.joss.05023.pdf
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Creating 10.21105.joss.05023.crossref.xml
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336 changes: 336 additions & 0 deletions joss.05023/10.21105.joss.05023.crossref.xml
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<citation key="cunningham2014dimensionality">
<article_title>Dimensionality reduction for large-scale
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<journal_title>Nature Neuroscience</journal_title>
<issue>11</issue>
<volume>17</volume>
<doi>10.1038/nn.3776</doi>
<cYear>2014</cYear>
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Nature Neuroscience, 17(11), 1500–1509.
https://doi.org/10.1038/nn.3776</unstructured_citation>
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<article_title>A large-scale neural network training
framework for generalized estimation of single-trial population
dynamics</article_title>
<author>Keshtkaran</author>
<journal_title>Nature Methods</journal_title>
<issue>12</issue>
<volume>19</volume>
<doi>10.1038/s41592-022-01675-0</doi>
<cYear>2022</cYear>
<unstructured_citation>Keshtkaran, M. R., Sedler, A. R.,
Chowdhury, R. H., Tandon, R., Basrai, D., Nguyen, S. L., Sohn, H.,
Jazayeri, M., Miller, L. E., &amp; Pandarinath, C. (2022). A large-scale
neural network training framework for generalized estimation of
single-trial population dynamics. Nature Methods, 19(12), 1572–1577.
https://doi.org/10.1038/s41592-022-01675-0</unstructured_citation>
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<article_title>Inferring single-trial neural population
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<author>Pandarinath</author>
<journal_title>Nature Methods</journal_title>
<issue>10</issue>
<volume>15</volume>
<doi>10.1038/s41592-018-0109-9</doi>
<cYear>2018</cYear>
<unstructured_citation>Pandarinath, C., O’Shea, D. J.,
Collins, J., Jozefowicz, R., Stavisky, S. D., Kao, J. C., Trautmann, E.
M., Kaufman, M. T., Ryu, S. I., Hochberg, L. R., &amp; others. (2018).
Inferring single-trial neural population dynamics using sequential
auto-encoders. Nature Methods, 15(10), 805–815.
https://doi.org/10.1038/s41592-018-0109-9</unstructured_citation>
</citation>
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<article_title>Population based training of neural
networks</article_title>
<author>Jaderberg</author>
<doi>10.48550/ARXIV.2201.11941</doi>
<cYear>2017</cYear>
<unstructured_citation>Jaderberg, M., Dalibard, V.,
Osindero, S., Czarnecki, W. M., Donahue, J., Razavi, A., Vinyals, O.,
Green, T., Dunning, I., Simonyan, K., &amp; others. (2017). Population
based training of neural networks.
https://doi.org/10.48550/ARXIV.2201.11941</unstructured_citation>
</citation>
<citation key="moritz2018ray">
<article_title>Ray: A distributed framework for emerging
\{AI\} applications</article_title>
<author>Moritz</author>
<journal_title>13th USENIX symposium on operating systems
design and implementation (OSDI 18)</journal_title>
<doi>10.48550/arXiv.1712.05889</doi>
<cYear>2018</cYear>
<unstructured_citation>Moritz, P., Nishihara, R., Wang, S.,
Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W.,
Jordan, M. I., &amp; others. (2018). Ray: A distributed framework for
emerging \{AI\} applications. 13th USENIX Symposium on Operating Systems
Design and Implementation (OSDI 18), 561–577.
https://doi.org/10.48550/arXiv.1712.05889</unstructured_citation>
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<author>George</author>
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<cYear>2020</cYear>
<unstructured_citation>George, J., Gao, C., Liu, R., Liu, H.
G., Tang, Y., Pydipaty, R., &amp; Saha, A. K. (2020). A scalable and
cloud-native hyperparameter tuning system.
https://doi.org/10.48550/arXiv.2006.02085</unstructured_citation>
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set]. DANDI archive.
https://dandiarchive.org/dandiset/000128/0.220113.0400</unstructured_citation>
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Park, I. M., Dyer, E., &amp; Pandarinath, C. (2021). Neural latents
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activity. In J. Vanschoren &amp; S. Yeung (Eds.), Proceedings of the
neural information processing systems track on datasets and benchmarks
(Vol. 1).
https://doi.org/10.48550/arXiv.2109.04463</unstructured_citation>
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<citation key="keshtkaran2019enabling">
<article_title>Enabling hyperparameter optimization in
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<author>Keshtkaran</author>
<journal_title>Advances in neural information processing
systems</journal_title>
<volume>32</volume>
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C. (2019). Enabling hyperparameter optimization in sequential
autoencoders for spiking neural data. In H. Wallach, H. Larochelle, A.
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neural information processing systems (Vol. 32). Curran Associates, Inc.
https://proceedings.neurips.cc/paper/2019/file/6948bd44c91acd2b54ecdd1b132f10fb-Paper.pdf</unstructured_citation>
</citation>
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via handwriting</article_title>
<author>Willett</author>
<journal_title>Nature</journal_title>
<issue>7858</issue>
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<cYear>2021</cYear>
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Hochberg, L. R., Henderson, J. M., &amp; Shenoy, K. V. (2021).
High-performance brain-to-text communication via handwriting. Nature,
593(7858), 249–254.
https://doi.org/10.1038/s41586-021-03506-2</unstructured_citation>
</citation>
<citation key="yu2021fast">
<article_title>Fast deep neural correspondence for tracking
and identifying neurons in c. Elegans using semi-synthetic
training</article_title>
<author>Yu</author>
<journal_title>eLife</journal_title>
<volume>10</volume>
<doi>10.7554/eLife.66410</doi>
<cYear>2021</cYear>
<unstructured_citation>Yu, X., Creamer, M. S., Randi, F.,
Sharma, A. K., Linderman, S. W., &amp; Leifer, A. M. (2021). Fast deep
neural correspondence for tracking and identifying neurons in c. Elegans
using semi-synthetic training. eLife, 10, e66410.
https://doi.org/10.7554/eLife.66410</unstructured_citation>
</citation>
<citation key="vyas2020computation">
<article_title>Computation through neural population
dynamics</article_title>
<author>Vyas</author>
<journal_title>Annual Review of Neuroscience</journal_title>
<volume>43</volume>
<doi>10.1146/annurev-neuro-092619-094115</doi>
<cYear>2020</cYear>
<unstructured_citation>Vyas, S., Golub, M. D., Sussillo, D.,
&amp; Shenoy, K. V. (2020). Computation through neural population
dynamics. Annual Review of Neuroscience, 43, 249.
https://doi.org/10.1146/annurev-neuro-092619-094115</unstructured_citation>
</citation>
<citation key="golub2018learning">
<article_title>Learning by neural
reassociation</article_title>
<author>Golub</author>
<journal_title>Nature Neuroscience</journal_title>
<issue>4</issue>
<volume>21</volume>
<doi>10.1038/s41593-018-0095-3</doi>
<cYear>2018</cYear>
<unstructured_citation>Golub, M. D., Sadtler, P. T., Oby, E.
R., Quick, K. M., Ryu, S. I., Tyler-Kabara, E. C., Batista, A. P.,
Chase, S. M., &amp; Yu, B. M. (2018). Learning by neural reassociation.
Nature Neuroscience, 21(4), 607–616.
https://doi.org/10.1038/s41593-018-0095-3</unstructured_citation>
</citation>
<citation key="kubeflow">
<article_title>Kubeflow: Machine learning toolkit for
kubernetes</article_title>
<cYear>2018</cYear>
<unstructured_citation>Kubeflow: Machine learning toolkit
for kubernetes (Version
swh:1:dir:086e4c66360c96571dccaa8d12645d4316a6b991). (2018).
https:/kubeflow/kubeflow</unstructured_citation>
</citation>
<citation key="vu2018shared">
<article_title>A shared vision for machine learning in
neuroscience</article_title>
<author>Vu</author>
<journal_title>Journal of Neuroscience</journal_title>
<issue>7</issue>
<volume>38</volume>
<doi>10.1523/JNEUROSCI.0508-17.2018</doi>
<cYear>2018</cYear>
<unstructured_citation>Vu, M.-A. T., Adalı, T., Ba, D.,
Buzsáki, G., Carlson, D., Heller, K., Liston, C., Rudin, C., Sohal, V.
S., Widge, A. S., &amp; others. (2018). A shared vision for machine
learning in neuroscience. Journal of Neuroscience, 38(7), 1601–1607.
https://doi.org/10.1523/JNEUROSCI.0508-17.2018</unstructured_citation>
</citation>
<citation key="credit">
<article_title>Beyond authorship: Attribution, contribution,
collaboration, and credit</article_title>
<author>Brand</author>
<journal_title>Learned Publishing</journal_title>
<issue>2</issue>
<volume>28</volume>
<doi>10.1087/20150211</doi>
<cYear>2015</cYear>
<unstructured_citation>Brand, A., Allen, L., Altman, M.,
Hlava, M., &amp; Scott, J. (2015). Beyond authorship: Attribution,
contribution, collaboration, and credit. Learned Publishing, 28(2),
151–155. https://doi.org/10.1087/20150211</unstructured_citation>
</citation>
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