Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex
Fig 2
Predictive coding of natural videos and learned space-time receptive fields.
(a) Inference on an example input image sequence of 10 frames. Top to bottom: Input sequence; model’s prediction of the current input from the previous step (the first step prediction being zero); prediction error (predicted input subtracted from the actual input); model’s final estimate of the current input after prediction error minimization. (b) The trained DPC network’s response to the natural image sequence in (a). Each plotted line represents the responses of a model neuron over 10 time steps. Top: responses of the 20 most active lower-level neurons (some colors are repeated); middle: responses of seven randomly chosen higher-level neurons; bottom: predicted transition dynamics (each line is the modulation weight for a basis transition matrix at the lower level). (c) 40 example spatial receptive fields (RFs) learned from natural videos. Each square tile is a column of U reshaped to a 16 × 16 image. (d) Space-Time RFs (STRFs) of four example lower-level neurons. First column: the spatial RFs of the example neurons. Next seven columns: the STRFs of the example neurons revealed by reverse correlation mapping. (e) Left panel: space-time plots of the example neurons in (d). Right panel: space-time plots of the RFs of two simple cells in the primary visual cortex of a cat (adapted from [25]).