Evaluation Summary:
This is a valuable paper that uses solid computational modeling approaches to link plasticity in the hippocampal circuit with behavioral learning. The work focuses on reinforcement learning, a theoretical framework for how animals can optimize learning by extracting the statistical structure of the sensory environments. While a vast range of experimental data regarding the physiological properties of neurons in the hippocampus exists, reinforcement learning often lacks such physiological details. The manuscript begins to fill this gap, by developing a spiking computational model of the hippocampus that can implement reinforcement learning and capture some features of hippocampal physiology.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)