Journal Article

Recurrent architecture for adaptive regulation of learning in the insect brain

Valdes-Aleman, Javier, Eschbach, Claire, Fushiki Akira, Arruda, Rebecca, Ohyama Tomoko, Zlatic Marta, Cardona, Albert, Winding, Michael, Schneider-Mizell, Casey M, Truman, James W, Shao Mei, Gerber Bertram, Fetter, Richard D, Litwin-Kumar, Ashok, Eichler Katharina, Thum, Andreas S

2020

Overview

Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.Eschbach, Fushiki et al. combine synaptic-resolution circuit mapping, functional analyses and modeling to reveal circuit motifs that regulate dopaminergic neuron activity and may increase associative learning task performance and flexibility.

• Publisher: Nature Publishing Group
• Subject: Adaptive learning / Associative learning / Circuits / Cognitive tasks / Computational neuroscience / Dopamine receptors / Feedback / Flexibility / Fruit flies / Insects / Larvae / Learning / Mapping / Neurons / Sensory neurons / Upstream