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Article Dans Une Revue Nature Neuroscience Année : 2020

Recurrent architecture for adaptive regulation of learning in the insect brain

Akira Fushiki
  • Fonction : Auteur
Michael Winding
  • Fonction : Auteur
Casey Schneider-Mizell
Mei Shao
  • Fonction : Auteur
Rebecca Arruda
  • Fonction : Auteur
Katharina Eichler
Javier Valdes-Aleman
Tomoko Ohyama
  • Fonction : Auteur
Andreas Thum
Bertram Gerber
Richard Fetter
James Truman
  • Fonction : Auteur
Ashok Litwin-Kumar
Albert Cardona
Marta Zlatic

Résumé

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.

Dates et versions

hal-04193610 , version 1 (01-09-2023)

Identifiants

Citer

Claire Eschbach, Akira Fushiki, Michael Winding, Casey Schneider-Mizell, Mei Shao, et al.. Recurrent architecture for adaptive regulation of learning in the insect brain. Nature Neuroscience, 2020, 23 (4), pp.544-555. ⟨10.1038/s41593-020-0607-9⟩. ⟨hal-04193610⟩
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