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Recurrent architecture for adaptive regulation of learning in the insect brain.

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Title: Recurrent architecture for adaptive regulation of learning in the insect brain.
Authors: Eschbach, Claire (AUTHOR), Fushiki, Akira (AUTHOR), Winding, Michael (AUTHOR), Schneider-Mizell, Casey M. (AUTHOR), Shao, Mei (AUTHOR), Arruda, Rebecca (AUTHOR), Eichler, Katharina (AUTHOR), Valdes-Aleman, Javier (AUTHOR), Ohyama, Tomoko (AUTHOR), Thum, Andreas S. (AUTHOR), Gerber, Bertram (AUTHOR), Fetter, Richard D. (AUTHOR), Truman, James W. (AUTHOR), Litwin-Kumar, Ashok (AUTHOR), Cardona, Albert (AUTHOR), Zlatic, Marta (AUTHOR)
Source: Nature Neuroscience. Apr2020, Vol. 23 Issue 4, p544-555. 12p. 3 Diagrams, 4 Graphs.
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 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. [ABSTRACT FROM AUTHOR]
Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="JN" term="%22Nature+Neuroscience%22">Nature Neuroscience</searchLink>. Apr2020, Vol. 23 Issue 4, p544-555. 12p. 3 Diagrams, 4 Graphs.
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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