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Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex

Fig 5

Cue-triggered activity recall in the DPC model.

(a) The experimental setup of Xu et al. (adapted from [1]). A bright dot stimulus moved from START to END repeatedly during conditioning. Activities of neurons whose receptive fields (colored ellipses) were along the dot’s trajectory were recorded. (b) Generative model combining an associative memory and DPC. The red part denotes the augmented memory component that binds the initial content vector r₀ and the dynamics vector r^h to encode an episodic memory. (c) Depiction of the memory encoding process. The presynaptic memory activity and postsynaptic prediction error jointly shape the memory weights G. (d) Depiction of the recall process. Prediction error on the partial observation drives the convergence of the memory estimates and recalls the higher-level dynamics vector as a top-down prediction. The red dotted box depicts the prediction error between the missing observations for r^h and the prediction ; this error is ignored during recall, implementing a form of robust predictive coding [49]. (e) The image sequence used to simulate conditioning and testing for our memory-augmented DPC network. (f) Responses of the lower-level neurons of the network. Colored lines represent the five most active lower-level neurons at each step. Left to right: neural responses during conditioning, testing the network with a single start frame, middle frame, and end frame. (g, h) Normalized pairwise cross correlation of (g) primary visual cortex neurons (adapted from [1]) and (h) the lower-level model neurons. Top: during conditioning; middle two: testing with the starting stimulus, before and after conditioning; bottom: the differences between cross correlations, “After” minus “Before” conditioning.

doi: https://doi.org/10.1371/journal.pcbi.1011801.g005