Switching Operation Modes in the Neocortex via Cholinergic Neuromodulation: A Computational Model of Uncertainty, Learning, and Inhibition

In order to deal with the uncertainty in the world, our brains need to be able to flexibly switch between the exploration of new sensory representations and exploitation of previously acquired ones. This requires forming accurate estimations of what and how much something is expected. While modeling has allowed for the development of several ways to form predictions, how the brain could implement those is still under debate. Here, we recognize acetylcholine as one of the main neuromodulators driving learning based on uncertainty, promoting the exploration of new sensory representations. We identify its interactions with cortical inhibitory interneurons and derive a biophysically grounded computational model able to capture and learn from uncertainty. This model allows us to understand inhibition beyond gain control by suggesting that different interneuron subtypes either encode predictions or estimate their uncertainty, facilitating detection of unexpected cues. Moreover, we show how acetylcholine-like neuromodulation uniquely interacts with global and local sources of inhibition, disrupting perceptual certainty and promoting the rapid acquisition of new perceptual cues. Altogether, our model proposes that cortical acetylcholine favors sensory exploration over exploitation in a cortical microcircuit dedicated to estimating sensory uncertainty.