Three-level DPC model learns progressively more abstract temporal representations.

(a) Generative model for three-level DPC. (b) Schematic depiction of an inference process. Observation nodes are omitted for clarity. (c) Inference for an example Moving MNIST sequence with “straight bouncing” dynamics. Red time steps mark the moments when the first-level prediction error exceeded the threshold, causing the network to transition to a new second-level state (see Methods). For these time steps, the predictions (second row) are by the second-level neurons, while the rest are by the first-level neurons as in Fig 3. (d) The network’s responses to the Moving MNIST sequence in (c). Left to right: first-level responses, second-level responses, third-level responses, first-level modulation weights, second-level modulation weights. (e) Same as (d) but with “clockwise bouncing” dynamics. (f) Same as (d) but for the sequence in (e). (g) Third-level responses to the Moving MNIST sequences visualized in the 2D space of the first two principal components. Left: responses colored according to bouncing type; right: responses colored according to motion direction. (h) Comparison of decoding performance for bouncing type versus motion direction using the modulation weights generated by the second and third level. Error bars: ±1 standard deviation from 10-fold cross validation. Orange: chance accuracies.