Extended Data Fig. 5: 2D visualizations under varying BCNE configurations and balance parameters. | Nature Computational Science

Extended Data Fig. 5: 2D visualizations under varying BCNE configurations and balance parameters.

From: Revealing neurocognitive and behavioral patterns through unsupervised manifold learning of dynamic brain data

Extended Data Fig. 5: 2D visualizations under varying BCNE configurations and balance parameters.The alternative text for this image may have been generated using AI.

This figure compares BCNE-generated 2D embeddings of the Sherlock dataset under different architectural configurations (top) and a range of balance-parameter settings (bottom). Each panel shows the low-dimensional trajectory for one of the four ROIs (EA, EV, HV and PMC) at recursion stages 0 and 3. Model-structure experiments evaluate the influence of alternative convolutional and dense-layer designs, while balance-parameter experiments assess the effect of varying the allocation ratio between HD- and LD-manifold components during training. Colormaps follow the same scene-label scheme as in Fig. 2.

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