英国剑桥大学Marta Zlatic、Albert Cardona以及美国哥伦比亚大学Ashok Litwin-Kumar合作取得一项新突破。他们揭示昆虫大脑中学习适应性调节的回路结构。该研究于2020年3月23日发表于国际学术期刊《自然-神经科学》杂志上。

他们在学习中心（果蝇幼虫的蘑菇体）的所有DAN上游，组建了回路的突触分辨率连接组。他们发现传入的感觉通路和大量神经元，这些神经元提供了蘑菇体输出神经元的反馈并连接了不同的记忆系统（厌恶性和食欲性）。他们将其与DAN及其突触前伴侣的功能研究以及综合回路模型相结合。他们发现DAN比较了来自厌恶性和食欲性系统的收敛反馈，这使得能够计算集成预测，从而可以改善未来的学习。计算模型表明，发现的反馈主题可以提高模型的灵活性和学习任务的性能。他们的研究提供了迄今为止支持关联学习的生物回路主题的最详细视图。

研究人员表示，多巴胺能神经元（DAN）推动了整个动物界的学习，但是调节其活动从而学习的上游回路仍然知之甚少。

附：英文原文

Title: Recurrent architecture for adaptive regulation of learning in the insect brain

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

Issue&Volume: 2020-03-23

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.

DOI: 10.1038/s41593-020-0607-9

Source: https://www.nature.com/articles/s41593-020-0607-9