@article {69783,
	title = {Cortical control of innate behavior from subcortical demonstration},
	journal = {bioRxiv},
	year = {2025},
	month = {2025 Feb 13},
	abstract = {<p>Motor control in mammals is traditionally viewed as a hierarchy of descending spinal-targeting pathways, with frontal cortex at the top 1{\textendash}3. Many redundant muscle patterns can solve a given task, and this high dimensionality allows flexibility but poses a problem for efficient learning 4. Although a feasible solution invokes subcortical innate motor patterns, or primitives, to reduce the dimensionality of the control problem, how cortex learns to utilize such primitives remains an open question 5{\textendash}7. To address this, we studied cortical and subcortical interactions as head-fixed mice learned contextual control of innate hindlimb extension behavior. Na{\"\i}ve mice performed reactive extensions to turn off a cold air stimulus within seconds and, using predictive cues, learned to avoid the stimulus altogether in tens of trials. Optogenetic inhibition of large areas of rostral cortex completely prevented avoidance behavior, but did not impair hindlimb extensions in reaction to the cold air stimulus. Remarkably, mice covertly learned to avoid the cold stimulus even without any prior experience of successful, cortically-mediated avoidance. These findings support a dynamic, heterarchical model in which the dominant locus of control can change, on the order of seconds, between cortical and subcortical brain areas. We propose that cortex can leverage periods when subcortex predominates as demonstrations, to learn parameterized control of innate behavioral primitives.</p>
},
	doi = {10.1101/2025.02.12.637930},
	url = {https://www.biorxiv.org/content/early/2025/02/13/2025.02.12.637930},
	author = {Keller, Jason A. and Kwak, Iljung S. and Stark, Alyssa K. and Pachitariu, Marius and Branson, Kristin and Dudman, Joshua T.}
}
@conference {65612,
	title = {Detecting the Starting Frame of Actions in Video},
	booktitle = {2020 IEEE Winter Conference on Applications of Computer Vision (WACV)},
	year = {2020},
	month = {2020 May 14},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Snowmass Village, CO, USA},
	abstract = {<p>In this work, we address the problem of precisely localizing key frames of an action, for example, the precise time that a pitcher releases a baseball, or the precise time that a crowd begins to applaud. Key frame localization is a largely overlooked and important action-recognition problem, for example in the field of neuroscience, in which we would like to understand the neural activity that produces the start of a bout of an action. To address this problem, we introduce a novel structured loss function that properly weights the types of errors that matter in such applications: it more heavily penalizes extra and missed action start detections over small misalignments. Our structured loss is based on the best matching between predicted and labeled action starts. We train recurrent neural networks (RNNs) to minimize differentiable approximations of this loss. To evaluate these methods, we introduce the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was collected and labeled by experts for the purpose of neuroscience research. On this dataset, we demonstrate that our method outperforms related approaches and baseline methods using an unstructured loss.</p>
},
	doi = {10.1109/WACV45572.202010.1109/WACV45572.2020.9093405},
	url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9087828},
	author = {Kwak, Iljung S. and Guo, Jian-Zhong and Hantman, Adam and Branson, Kristin and Kriegman, David}
}
