Abstract
Decision making strategies guided by observable stimuli and those that also require inferences about unobserved states have been linked to distinct computational requirements and neural substrates. Here, we formulate a model based on temporal integration and reset that incorporates both strategies into a unified family of decision algorithms. We show, using recordings from the frontal cortex of mice performing a foraging task, that the entire family of algorithms can be simultaneously decoded from the same neural ensemble, regardless of the one concurrently executed by the mice. Thus, using multiplexed integration, the cortex may avoid premature commitment to a single algorithm and maintain multiple decision strategies in parallel.
Competing Interest Statement
The authors have declared no competing interest.