Data-driven analyses of motor impairments in animal models of neurological disorders
Fig 9
A network trained to predict stroke size discovered the same most informative movement features as the network trained to predict expert scores.
(A) Network predictions of stroke lesion volume (normalized [“Norm.”] between 0 and 1). The line shows linear regression. (B) Importance of movement features as determined by the network trained on stroke size (y-axis) and the network trained on expert scores (x-axis). Each point represents one of 2,048 features from the output of the ConvNet (Fig 1). (C) Representation of video frames in internal feature space of the network trained to predict stroke volume (see Fig 4A for description). Green and black points correspond to frames identified in previous analyses (see Fig 5) as belonging to reaching with the mouth and eating with both hands (outlined with dashed ellipses). The similar location of those clusters to the corresponding ones in Fig 5 exemplifies the discovery of similar feature importance by both networks. The sample network and data on which this figure is based are available at github.com/hardeepsryait/behaviour_net. ConvNet, convolutional network; PC, principal component.