Neuronal Computation Underlying Inferential Reasoning in Humans and Mice.

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Decision-making is crucial for survival and adaptation.
• Inferential decisions, based on novel choices and related events, engage multiple brain regions.
• The neuronal computation underlying these inferential decisions remains largely unknown.

Purpose of the Study:

• To investigate the functional anatomy and neuronal computation of inferential decision-making.
• To elucidate the brain mechanisms enabling predictions about novel choices.

Main Methods:

• A multi-day, cross-species approach using both human and mouse models.
• Analysis of hippocampal activity during successful inference and resting behavior.

Main Results:

• The mammalian brain employs a hippocampal prospective code for successful inference.
• This code forecasts temporally structured learned associations.
• During rest, hippocampal sharp-wave/ripples coactivate cells representing inferred reward relationships, effectively connecting unobserved but profitable events.

Conclusions:

• The hippocampus plays a key role in inferential decision-making by forecasting associations.
• Coactivation of hippocampal cells during sharp-wave/ripples facilitates "joining-the-dots" between events, even if not directly experienced.
• This mechanism supports the formation of cognitive maps beyond direct experience, enabling flexible behavior.