Flexible information routing in neural populations through stochastic comodulation

Humans and animals are capable of flexibly switching between a multitude of tasks, each requiring rapid, sensory-informed decision making. Incoming stimuli are processed by a hierarchy of neural circuits consisting of millions of neurons with diverse feature selectivity. At any given moment, only a small subset of these carry task-relevant information. In principle, downstream processing stages could identify the relevant neurons through supervised learning, but this would require many example trials. Such extensive learning periods are inconsistent with the observed flexibility of humans or animals, who can adjust to changes in task parameters or structure almost immediately. Here, we propose a novel solution based on functionally-targeted stochastic modulation.