A Multi-Region Brain Model to Elucidate the Role of Hippocampus in Spatially Embedded Decision-Making

Brains excel at robust decision-making and data-efficient learning. Understanding the architectures and dynamics underlying these capabilities can inform inductive biases for deep learning. We present a multi-region brain model that explores the normative role of structured memory circuits in a spatially embedded binary decision-making task from neuroscience.We counterfactually compare the learning performance and neural representations of reinforcement learning (RL) agents with brain models of different interaction architectures between grid and place cells in the entorhinal cortex and hippocampus, coupled with an action-selection cortical recurrent neural network. We demonstrate that a specific architecture--where grid cells receive and jointly encode self-movement velocity signals and decision evidence increments--optimizes learning efficiency while best reproducing experimental observations relative to alternative architectures.Our findings thus suggest brain-inspired structured architectures for efficient RL. Importantly, the models make novel, testable predictions about organization and information flow within the entorhinal-hippocampal-neocortical circuit: we predict that grid cells must conjunctively encode position and evidence for effective spatial decision-making, directly motivating new neurophysiological experiments.

Everyday choices—like deciding when to turn down a hallway—rely on two skills: knowing where we are and adding up clues about what to do next. Yet, it remains a mystery how the brain merges these streams of information across multiple brain regions to accomplish this everyday task of making decisions in physical space.We built a “virtual brain” inside a reinforcement‑learning agent that links three key brain circuits: grid cells that track location, the hippocampus that stores memories, and a small cortical network that picks actions. The agent practises a classic mouse task: walking down a T‑shaped maze lined with visual towers ("evidence") on both sides, then turning at the end toward the side with more towers (“decision”).After testing all the possible brain circuit designs, we found that learning was fastest—and runs were shortest—when each grid cell encoded both position and a running tower count. This “dual‑coding” design also reproduced the unusual firing patterns recorded in the real mouse hippocampus during the same task, whereas other designs did not.For neuroscience, our results predict that biological grid cells combine “Where am I?” with “How much evidence have I gathered?”—a hypothesis that future neurophysiology experiments can directly test. For machine learning, adding such structured memory maps to learning agents can reduce training demands, enabling lighter, more efficient AI that makes decisions in the real world.