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

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Recurrent architecture for adaptive regulation of learning in the insect brain.
Nature Neuroscience ( IF 21.2 ) Pub Date : 2020-03-23 , DOI: 10.1038/s41593-020-0607-9
Claire Eschbach _{1,

2} , Akira Fushiki _{1,

3} , Michael Winding _{1,

2} , Casey M Schneider-Mizell _{1,

4} , Mei Shao ₁ , Rebecca Arruda ₁ , Katharina Eichler _{1,

5} , Javier Valdes-Aleman ₁ , Tomoko Ohyama _{1,

6} , Andreas S Thum ₇ , Bertram Gerber ₈ , Richard D Fetter ₁ , James W Truman _{1,

9} , Ashok Litwin-Kumar ₁₀ , Albert Cardona _{1,

11,

12} , Marta Zlatic _{1,

2,

11}

Affiliation

HHMI Janelia Research Campus, Ashburn, VA, USA.
Department of Zoology, University of Cambridge, Cambridge, UK.
Departments of Neuroscience and Neurology, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
Allen Institute for Brain Science, Seattle, WA, USA.
Institute of Neurobiology, University of Puerto Rico Medical Science Campus, San Juan, Puerto Rico, USA.
Department of Biology, McGill University, Montreal, Quebec, Canada.
Department of Genetics, Institute for Biology, University of Leipzig, Leipzig, Germany.
Abteilung Genetik von Lernen & Gedächtnis, Leibniz Institut für Neurobiologie, Otto von Guericke University Magdeburg, Institut für Biologie, Verhaltensgenetik, & Center for Behavioral Brain Sciences, Magdeburg, Germany.
Department of Biology, University of Washington, Seattle, WA, USA.
Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
MRC Laboratory of Molecular Biology, Cambridge, UK.
Department of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, UK.

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.

中文翻译：

昆虫大脑学习适应性调节的循环架构。

多巴胺能神经元 (DAN) 驱动整个动物界的学习，但调节其活动并从而实现学习的上游回路仍然知之甚少。我们在学习中心（果蝇幼虫的蘑菇体）中提供了所有 DAN 上游电路的突触分辨率连接组。我们发现传入感觉通路和大量神经元，它们提供来自蘑菇体输出神经元的反馈并连接不同的记忆系统（厌恶和食欲）。我们将其与 DAN 及其突触前伙伴的功能研究以及综合电路建模相结合。我们发现 DAN 会比较来自厌恶系统和食欲系统的收敛反馈，从而能够计算综合预测，从而改善未来的学习。计算建模表明，发现的反馈主题提高了模型的灵活性和学习任务的性能。我们的研究提供了迄今为止支持联想学习的生物回路图案的最详细视图。

更新日期：2020-03-23

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