Rules and Mechanisms for Efficient Two-Stage Learning in Neural Circuits

Authors

Tiberiu Teşileanu, CUNY Graduate Center, University of Pennsylvania
Bence Ölveczky, Harvard University
Vijay Balasubramanian, CUNY Graduate Center, University of PennsylvaniaFollow

Document Type

Article

Publication Date

2017

Abstract

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-related 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.

Comments

This article was originally published in eLife on April 4, 2017 (6:e20944) under a Creative Commons Attribution License.

DOI: http://dx.doi.org/10.7554/eLife.20944