It is generally believed that the hippocampus plays a crucial role in declarative memory—remembering facts and events—but not in gradual stimulus-response association or incremental value learning. Based on the finding that CA1 conveys strong value signals during dynamic foraging, we investigated the possibility that the hippocampus contributes to incremental value learning. Specifically, we examined effects of inactivating different subregions of the dorsal hippocampus on behavioral performance of mice performing a dynamic foraging task in a modified T-maze. A reinforcement learning model-based analysis indicated that inactivation of CA1, but not dentate gyrus, CA3, or CA2, impaired trial-by-trial updating of chosen value without affecting value-dependent action selection. As a result, it took longer for CA1-inactivated mice to bias their choices toward the higher-reward-probability target after changes in reward probability. Our results indicate, contrary to the traditional view, that the hippocampus, especially CA1, might contribute to incremental value learning under certain circumstances.

Despite tremendous progress, the neural circuit dynamics underlying hippocampal mnemonic processing remain poorly understood. We propose a new model for hippocampal function-the simulation-selection model-based on recent experimental findings and neuroecological considerations. Under this model, the mammalian hippocampus evolved to simulate and evaluate arbitrary navigation sequences. Specifically, we suggest that CA3 simulates unexperienced navigation sequences in addition to remembering experienced ones, and CA1 selects from among these CA3-generated sequences, reinforcing those that are likely to maximize reward during offline idling states. High-value sequences reinforced in CA1 may allow flexible navigation toward a potential rewarding location during subsequent navigation. We argue that the simulation-selection functions of the hippocampus have evolved in mammals mostly because of the unique navigational needs of land mammals. Our model may account for why the mammalian hippocampus has evolved not only to remember, but also to imagine episodes, and how this might be implemented in its neural circuits.

Stroke is one of the leading causes of death and disability worldwide. Stroke is treatable, but it is prone to disability after treatment. To grasp the degree of disability caused by stroke, we use magnetic resonance imaging text records to predict stroke and measure the performance according to the document-level and sentence-level representation. As a result of the experiment, the document-level representation shows better performance.

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