Neural manifold under plasticity in a goal driven learning behaviour
Fig 2
RNN with correct feedback signal can learn within- and outside-manifold equally well.
(A) Cursor trajectories after initial training phase (on the left), after within- (WMP) or outside-manifold (OMP) perturbation (in the middle) and after within (WMR) or outside (OMR) retraining phase (on the right) for one example simulation. (B) Performance measured as mean squared error (MSE) between target cursor velocity and produced cursor velocity. Shown are the average simulation results for twenty randomly initialized networks, bars indicate the standard deviation across networks. (C) Weight distribution and weight change distribution during the experiment. The inlet shows the average standard deviation of weight change, bars indicate standard deviation across networks. (D) Manifold overlap between before and after retraining phase (red and blue), or between target manifold for outside-manifold perturbation and internal manifold after retraining phase (green). Inlet shows how the overlap between BCI target manifold and internal manifold depends on BCI manifold dimension.