Neural manifold under plasticity in a goal driven learning behaviour
Fig 3
RNN with corrupted feedback signal or constraint on weight change can learn better within- than outside-manifold.
(A) Relearning results for RNN with correct feedback signal (same as in Fig 2), bars indicate here and in the following standard deviation across networks. WMP and OMP show performance after within- or outside-manifold perturbations, whereas WMR and OMR show performance after retraining of the respective BCI perturbations (top). Manifold overlap is measured between initial manifold and manifold after within learning (I-WMR), initial manifold and manifold after outside learning (I-OMR) and manifold defined by BCI perturbation and manifold after outside learning (P-OMR) (center). (B) Relearning results for noisy feedback signal. The correct feedback transformation is distorted by adding independent noise to each entry of the matrix. The noise is drawn from a zero-mean Gaussian with standard deviation σ = α ⋅ σoriginal, where α is the noise factor and σoriginal is the standard deviation of the correct feedback matrix. Top panel shows performance results, center panel shows manifold overlap and bottom panel shows the relation between both. (C) Relearning results for sparse feedback signal. In this scenario not all neurons in the recurrent network receive a feedback signal, which leads to a portion of recurrent weights remaining static during retraining. (D) Relearning results for sparse plastic connections. Similar to (C) a portion of recurrent weights remain static during retraining. But in contrast to (C), these static connections are not clustered to specific neurons. Instead, all neurons keep at least one plastic incoming connection.