Functional Inference of Complex Anatomical Tendinous Networks at a Macroscopic Scale via Sparse Experimentation
Figure 5
Topology matters and so does informative data generated from the inference process.
Training set (left column) and cross validation (right column) errors when (a) the ‘AFH’ target is inferred and (b) when the ‘aWR’ network is inferred. Solid lines represent errors with sequential informative experimental data when both topologic and parametric inference is performed. Dotted lines correspond to cases when random experimental data is sequentially employed for inference. Dashed lines show errors for only parametric inference with basic (Fig. S2 c–d) topologies. Error bars represent standard errors across three executions of the inference processes. Topologically and parametrically inferred networks with sequentially introduced informative force-motion data are more functionally proximal to the respective targets compared to (i) when random data is used and (ii) when module/string interconnectivity is ignored during inference.