Neural manifold under plasticity in a goal driven learning behaviour
Fig 5
Feedback signal for outside-manifold perturbation can potentially be learned with incremental strategy.
(A) Incremental outside-manifold perturbation, where with each incremental step α the BCI readout is less aligned with the original neural manifold. (B-C) Retraining results for incremental perturbation steps using correct feedback signal. (B) Task performance after retraining, measured as mean squared error (MSE), achieved with current incremental BCI readout (iOMP), which is the readout used during retraining, as well as achieved under the full outside-manifold perturbation (OMP). (C) Manifold overlap between initial manifold and manifold after relearning (blue line), as well as between target manifold, defined by the current BCI readout, and manifold after relearning (green line). (D-F) Retraining results using incremental strategy and assuming, that each consecutive incremental learning step starts from a correctly trained network. (D) Feedback learning performance, quantified by calculating the correlation coefficient between correct and inferred feedback weights, for each incremental learning step. (E) Task performance after recurrent retraining. (F) Manifold overlap for each incremental step. (G-I) Retraining results using full incremental strategy. Here, each consecutive learning step starts from the network state obtained in the previous step. Feedback learning performance (G), task performance after recurrent retraining (H) and manifold overlap (I).