CRCNS: Computational principles of mental simulation in the entorhinal and parietal cortex

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Humans make rich inferences about the relationships between entities in the world from scarce information. For example, we can find a novel destination after seeing a few street numbers, or find a page in a dictionary by glancing at a few words in other pages. Theoretical considerations suggest that the brain makes such inferences by constructing "internal models" of the relationships in the environment (relationships between actions and states of the world), and by mentally simulating those models. However, the neural substrates and mechanisms of mental simulation are not understood. Our overarching goal is to integrate insights from theory and modeling with behavior and electrophysiology in awake, behaving monkeys to understand how mental simulation of internal models support relational inference. We will develop a behavioral task for monkeys in which they have “navigate” mentally from one stimulus to another along a one-dimensional abstract space of discrete stimuli (i.e., a sequence of images). We will assess whether animals’ behavioral characteristics exhibit hallmarks of mental simulation. We will then create a large library of neural network models to generate hypotheses for alternative computational strategies (including mental simulations) that the brain might employ for navigating abstract spaces. Next, we will record from candidate brain areas in the parietal and entorhinal cortex of monkeys, and analyze the data at single cell and population levels looking for signatures of mental simulation. Finally, we will adopt an iterative approach involving model-based data analyses and data-driven model revision with the ultimate goal of creating models that simultaneously succeed in performing task-relevant computations (i.e., behavior) and account for observed neural responses. Finally, we will validate our framework by evaluating the predictions of our models for both behavior and electrophysiology in new behavioral tasks.