1. Recurrent architecture for adaptive regulation of learning in the insect brain
- Author
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Andreas S. Thum, Casey M Schneider-Mizell, James W Truman, Bertram Gerber, Tomoko Ohyama, Albert Cardona, Marta Zlatic, Akira Fushiki, Claire Eschbach, Javier Valdes-Aleman, Ashok Litwin-Kumar, Rebecca Arruda, Michael Winding, Mei Shao, Richard D. Fetter, and Katharina Eichler
- Subjects
0301 basic medicine ,Computer science ,Models, Neurological ,Sensory system ,Memory systems ,Article ,03 medical and health sciences ,0302 clinical medicine ,Memory ,Neural Pathways ,Animals ,Learning ,Upstream (networking) ,Mushroom Bodies ,Learning center ,General Neuroscience ,Dopaminergic Neurons ,Flexibility (personality) ,Associative learning ,030104 developmental biology ,Larva ,Mushroom bodies ,Connectome ,Drosophila ,Neuroscience ,030217 neurology & neurosurgery - Abstract
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 the first synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mush-room body (MB) of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from MB 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 modelling. 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 modelling 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.
- Published
- 2020
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