RT Journal Article
SR Electronic
T1 Complementary learning systems within the hippocampus: A neural network modeling approach to reconciling episodic memory with statistical learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 051870
DO 10.1101/051870
A1 Anna C. Schapiro
A1 Nicholas B. Turk-Browne
A1 Matthew M. Botvinick
A1 Kenneth A. Norman
YR 2016
UL http://biorxiv.org/content/early/2016/05/06/051870.abstract
AB A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway — the pathway connecting entorhinal cortex directly to region CA1 — was able to support statistical learning, while the trisynaptic pathway — connecting entorhinal cortex to CA1 through dentate gyrus and CA3 — learned only individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself.