RT Journal Article SR Electronic T1 Rules and mechanisms for efficient two-stage learning in neural circuits JF bioRxiv FD Cold Spring Harbor Laboratory SP 071910 DO 10.1101/071910 A1 Tiberiu Teşileanu A1 Bence Ölveczky A1 Vijay Balasubramanian YR 2017 UL http://biorxiv.org/content/early/2017/03/08/071910.abstract AB Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in ‘tutor’ circuits (e.g., LMAN) should match plasticity mechanisms in ‘student’ circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.