Functional Inference of Complex Anatomical Tendinous Networks at a Macroscopic Scale via Sparse Experimentation
Figure 6
Topology matters and so does informative data generated from the inference process.
Errors incurred during the inference of the finger extensor mechanism. (a) Training set errors. (b) Cross validation errors. Progression of the error values are depicted as the number of data sets are introduced for model evolution. Error values are depicted for five executions for the topologic and parametric inference using informative tests. Bars represent standard errors. Solid lines correspond to the mean error when informative data is employed in topological and parametric inference of the target. Dotted lines show mean errors when sequential random data sets are used (error bars depicted for 20 executions). Dashed lines represent mean errors (error bars depicted for 20 executions) when only parametric evolution is performed using the basic topology where in only 6 strings connect all accessible (input and output) ports and no other interconnection is allowed (Fig. S2 d). At the end of the inference process, the mean cross validation error when models are topologically inferred using informative tests is 6.8%, better (by about 3%) than parametric-only inference with informative tests and improved (by about 7%) compared to the topological inference with random tests. The topology chosen for parametric-only inference (Fig. S2 d) comprises most components of the Winslow's Rhombus (Fig. 4a) except for the lateral offshoots and transverse bands. We observe in Fig. 4e that some inferred models do resemble the Winslow's Rhombus structurally. The low difference (3%) in the mean cross validation errors suggests that the disparity between the topologically and parametrically inferred models and the parametric-only inferred models (both using informative tests) is minor but essentially topologic (we reckon this could be because of the absence of the lateral offshoots in the all-in-all topology in Fig. S2d). The difference of 7% in (b) between models inferred using informative and random tests is however, relatively significant and is expected to be larger with further improvements in the inference process (see discussion). As observed in [56], the margin gained with the estimation-exploration algorithm grows with the complexity of the problem. Additionally, one may require more number of trials (which will add to the temporal and computational cost) to achieve a certain accuracy. With the estimation-exploration algorithm, a given accuracy can be achieved faster.