Learning prediction error neurons in a canonical interneuron circuit.

ABSTRACT: Sensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative predi

SUBMITTER: Hertag L 

PROVIDER: S-EPMC7442488 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature