Recurrent predictive coding models for associative memory employing covariance learning.

ABSTRACT: The computational principles adopted by the hippocampus in associative memory (AM) tasks have been one of the most studied topics in computational and theoretical neuroscience. Recent theories suggested that AM and the predictive activities of the hippocampus could be described within a unitary account, and that predictive coding underlies the computations supporting AM in the hippocampus. Following this theory, a computational model based on classical hierarchical predictive networks was proposed and was shown to perform well in various AM tasks. However, this fully hierarchical model did not incorporate recurrent connections, an architectural component of the CA3 region of the hippocampus that is crucial for AM. This makes the structure of the model inconsistent with the known connectivi

SUBMITTER: Tang M 

PROVIDER: S-EPMC10132551 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature