Figure 1. Proposing a new behavior embedding evaluation metric: TPI (Temporal Proximity Index) (IMAGE)
Caption
(A) Displays the movement of a mouse's 3D action skeleton over time, with each color representing a standardized behavior repertoire (e.g., walking, standing).
(B) Once the behavior embedding space is created, examining the movement patterns over time can verify the quality of the embedding. A good embedding space has clusters that contain the same behavior states, showing efficient temporal movement, while a poor embedding space has clusters that contain different behavior states, resulting in inefficient temporal movement.
(C) The quality of temporal connectivity can be calculated by the total value of the product of transition probabilities between clusters and the distance between clusters (TPI). (Left) Frequent transitions to nearby clusters indicate good temporal connectivity. (Right) Few transitions to nearby clusters indicate poor temporal connectivity.
(D) Calculation method of the Temporal Proximity Index (TPI) for evaluating the temporal connectivity of the behavior embedding space.
(E) Workflow for unsupervised animal behavior analysis.
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Institute for Basic Science
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