Figure 2.

Three different models for learning-based goal-directed motor planning. (a) The forward model implemented in an RNN, (b) predictive coding (PC) and active inference (AIF) frameworks implemented in a recurrent neural network (RNN) using initial sensitivity by latent random variables at the initial step, either by the stochastic ${\mathit{z}}_t$ or the deterministic ${\mathit{d}}_t$, and (c) the proposed GLean scheme based on the PC and AIF framework implemented in a variational RNN. In each case, the horizontal axis indicates progression through time (left to right). The black arrows represent computation in the forward pass, while the red arrows represent prediction error being propagated during backpropagation through time (BPTT).