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paper/paper.md

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@@ -54,7 +54,7 @@ generated by a latent state, $z_t$, which evolve according to a transition
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$u_t$, to steer the latent state dynamics and influence the observations.
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For example, SSMs are often used in neuroscience to model the dynamics of
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neural spike train recordings [@vyas:2020]. Here, $y_t$ is a vector of spike
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neural spike train recordings [@vyas2020computation]. Here, $y_t$ is a vector of spike
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counts from each of, say, 100 measured neurons. The activity of nearby neurons
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is often correlated, and SSMs can capture that correlation through a lower
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dimensional latent state, $z_t$. Finally, if we know that certain sensory inputs
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in machine learning research [@zhao2023revisiting; @lee2023switching; @chang2023low].
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More sophisticated, special purpose models on top of `Dynamax`, like the
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Keypoint-MoSeq library for modeling postural dynamics
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of animals [@Weinreb:2024]. Finally, the `Dynamax` tutorials are used as reference
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of animals [@weinreb2024keypoint]. Finally, the `Dynamax` tutorials are used as reference
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examples in a major machine learning textbook [@murphy2023probabilistic].
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# Acknowledgements

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