A neural network model of hippocampal contributions to category learning

Abstract

AbstractIn addition to its critical role in encoding individual episodes, the hippocampus is capable of extracting regularities across experiences. This ability is central to category learning, and a growing literature indicates that the hippocampus indeed makes important contributions to this kind of learning. Using a neural network model that mirrors the anatomy of the hippocampus, we investigated the mechanisms by which the hippocampus may support novel category learning. We simulated three category learning paradigms and evaluated the network’s ability to categorize and to recognize specific exemplars in each. We found that the trisynaptic pathway within the hippocampus—connecting entorhinal cortex to dentate gyrus, CA3, and CA1—was critical for remembering individual exemplars, reflecting the rapid binding and pattern separation functions of this circuit. The monosynaptic pathway from entorhinal cortex to CA1, in contrast, was responsible for detecting the regularities that define category structure, made possible by the use of distributed representations and a slower learning rate. Together, the simulations provide an account of how the hippocampus and its constituent pathways support novel category learning.