Lead Scientists:

Jason Keller (Google Scholar, ORCID)

Josh Dudman (Google Scholar, ORCID)

PREPRINT

https://www.biorxiv.org/content/10.1101/2025.02.12.637930v1

CtxCtrlFromSubctxDemo.pdf

blueprint thread: https://bsky.app/profile/neurojak.bsky.social/post/3li3fnfvcik2r

ABSTRACT

Motor control in mammals is traditionally viewed as a hierarchy of descending spinal-targeting pathways, with frontal cortex at the top. Many redundant muscle patterns can solve a given task, and this high dimensionality allows flexibility but poses a problem for efficient learning. 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. To address this, we studied cortical and subcortical interactions as head-fixed mice learned contextual control of innate hindlimb extension behavior. Naïve 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.

SUPPLEMENTARY VIDEOS

SupplVid1_avoidReactExtensions.mp4

SupplVid2_localCtxInhibition.mp4

SupplVid3_acuteDecortication.mp4

PUBLIC DATA REPOSITORY

FigShare link & DOI coming soon

HARDWARE

2021_06_4probes2_glowingEdges.jpg

jfcRig.png

https://cad.onshape.com (search: “jumpLever” for rig or “miniRIVETS2” for headplates)

SOFTWARE

video capture: https://github.com/neurojak/pySpinCapture

LabVIEW behavior control: https://github.com/neurojak/jfcRigControl

MATLAB analysis code will be bundled with data and metadata in FigShare above, but for now is available at:

https://github.com/neurojak/CtxCtrlFromSubCtxDemo_AnalysisCode

KINEMATICS LABELING

APT_LabelInstructions.pdf