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

Autor: Jiang LP; Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington, United States of America.; Center for Neurotechnology, University of Washington, Seattle, Washington, United States of America.; Computational Neuroscience Center, University of Washington, Seattle, Washington, United States of America., Rao RPN; Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington, United States of America.; Center for Neurotechnology, University of Washington, Seattle, Washington, United States of America.; Computational Neuroscience Center, University of Washington, Seattle, Washington, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2024 Feb 08; Vol. 20 (2), pp. e1011801. Date of Electronic Publication: 2024 Feb 08 (Print Publication: 2024).
DOI: 10.1371/journal.pcbi.1011801
Abstrakt: We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the neocortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form representations that encode sequences at shorter timescales (e.g., a single step) while higher levels form representations that encode sequences at longer timescales (e.g., an entire sequence). We tested this model using a two-level neural network, where the top-down modulation creates low-dimensional combinations of a set of learned temporal dynamics to explain input sequences. When trained on natural videos, the lower-level model neurons developed space-time receptive fields similar to those of simple cells in the primary visual cortex while the higher-level responses spanned longer timescales, mimicking temporal response hierarchies in the cortex. Additionally, the network's hierarchical sequence representation exhibited both predictive and postdictive effects resembling those observed in visual motion processing in humans (e.g., in the flash-lag illusion). When coupled with an associative memory emulating the role of the hippocampus, the model allowed episodic memories to be stored and retrieved, supporting cue-triggered recall of an input sequence similar to activity recall in the visual cortex. When extended to three hierarchical levels, the model learned progressively more abstract temporal representations along the hierarchy. Taken together, our results suggest that cortical processing and learning of sequences can be interpreted as dynamic predictive coding based on a hierarchical spatiotemporal generative model of the visual world.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Jiang, Rao. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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