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Neural mechanisms of model-based planning in the rat

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If you have a question about this talk, please contact Marcelo Gomes Mattar.

Planning can be defined as use of an internal model, containing knowledge about the outcomes likely to follow each possible action, to guide action selection. In recent work, we adapted for rodents a multi-step decision task widely used to study planning in human subjects, allowing the extensive experimental toolkit available for rodents to be brought to bear on this problem in a new way. We found that rats adopt a strategy of model-based planning to solve this task, and that silencing neural activity in either the orbitofrontal cortex or the dorsal hippocampus was sufficient to impair planning. Here, I will describe new data from experiments designed to reveal the computational role in model-based cognition played by each region. In the orbitofrontal cortex (OFC), neurons encode information about expected outcomes in a manner specifically suitable for a role in model-based learning, but not for a role in model-based choice. Trial-by-trial optogenetic inactivations of the OFC similarly reveal a pattern of impairment that is consistent with impaired learning, but not with impaired decision-making. These data suggest that the OFC supports model-based cognition by signaling expected outcomes to a process which updates choice mechanisms residing elsewhere in the brain. In the dorsal hippocampus, activity of CA1 neurons does not seem to encode information about expected outcomes, but instead indexes the various behavioral states of the task in a manner reminiscent of “place cell” coding. Sharp wave ripple events which occur during the inter-trial interval show evidence of “replay”, in which sequences of neural activity typical of task performance (occupying several seconds) are rapidly repeated within the span of a single sharp-wave ripple (occupying severals tens of milliseconds). Ongoing work seeks to test whether the content of these replay events is consistent with computational theories proposing roles in learning, decision-making, or both.

This talk is part of the Computational Neuroscience series.

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