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a, Left, schematic of flexible decision-tree encoding model of unit activity. Coordinate axes extracted from tracked jaw and tongue <t>keypoints</t> were used to predict spike rates. Right, performance (10-fold cross-validated R ) of boosted tree models that used 6 jaw or 12 jaw+tongue axis weights. Gray indicates performance of linear models . b, Left, schematic of decision-tree encoding models after unsupervised extraction of kinematic features from the video data (Methods) , . Right, these models outperformed linear and boosted trees models that used keypoint-based data (a), however still did not explain large components of MSA activity.
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a, Left, schematic of flexible decision-tree encoding model of unit activity. Coordinate axes extracted from tracked jaw and tongue <t>keypoints</t> were used to predict spike rates. Right, performance (10-fold cross-validated R ) of boosted tree models that used 6 jaw or 12 jaw+tongue axis weights. Gray indicates performance of linear models . b, Left, schematic of decision-tree encoding models after unsupervised extraction of kinematic features from the video data (Methods) , . Right, these models outperformed linear and boosted trees models that used keypoint-based data (a), however still did not explain large components of MSA activity.
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a, Left, schematic of flexible decision-tree encoding model of unit activity. Coordinate axes extracted from tracked jaw and tongue <t>keypoints</t> were used to predict spike rates. Right, performance (10-fold cross-validated R ) of boosted tree models that used 6 jaw or 12 jaw+tongue axis weights. Gray indicates performance of linear models . b, Left, schematic of decision-tree encoding models after unsupervised extraction of kinematic features from the video data (Methods) , . Right, these models outperformed linear and boosted trees models that used keypoint-based data (a), however still did not explain large components of MSA activity.
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a, Left, schematic of flexible decision-tree encoding model of unit activity. Coordinate axes extracted from tracked jaw and tongue <t>keypoints</t> were used to predict spike rates. Right, performance (10-fold cross-validated R ) of boosted tree models that used 6 jaw or 12 jaw+tongue axis weights. Gray indicates performance of linear models . b, Left, schematic of decision-tree encoding models after unsupervised extraction of kinematic features from the video data (Methods) , . Right, these models outperformed linear and boosted trees models that used keypoint-based data (a), however still did not explain large components of MSA activity.
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Image Search Results


a, Left, schematic of flexible decision-tree encoding model of unit activity. Coordinate axes extracted from tracked jaw and tongue keypoints were used to predict spike rates. Right, performance (10-fold cross-validated R ) of boosted tree models that used 6 jaw or 12 jaw+tongue axis weights. Gray indicates performance of linear models . b, Left, schematic of decision-tree encoding models after unsupervised extraction of kinematic features from the video data (Methods) , . Right, these models outperformed linear and boosted trees models that used keypoint-based data (a), however still did not explain large components of MSA activity.

Journal: bioRxiv

Article Title: Muscle spindles provide flexible sensory feedback for movement sequences

doi: 10.1101/2024.09.13.612899

Figure Lengend Snippet: a, Left, schematic of flexible decision-tree encoding model of unit activity. Coordinate axes extracted from tracked jaw and tongue keypoints were used to predict spike rates. Right, performance (10-fold cross-validated R ) of boosted tree models that used 6 jaw or 12 jaw+tongue axis weights. Gray indicates performance of linear models . b, Left, schematic of decision-tree encoding models after unsupervised extraction of kinematic features from the video data (Methods) , . Right, these models outperformed linear and boosted trees models that used keypoint-based data (a), however still did not explain large components of MSA activity.

Article Snippet: Image stacks for each session were registered to a common cheek image (Supplementary Figure 2c,j) using manually selected keypoints (fitgeotrans in MATLAB, projective transformation).

Techniques: Activity Assay, Extraction