High-throughput automated methods for classical and operant conditioning of Drosophila larvae

Author:

Croteau-Chonka Elise C12ORCID,Clayton Michael S3ORCID,Venkatasubramanian Lalanti1ORCID,Harris Samuel N3,Jones Benjamin MW3,Narayan Lakshmi2,Winding Michael12,Masson Jean-Baptiste24,Zlatic Marta123ORCID,Klein Kristina T12ORCID

Affiliation:

1. Department of Zoology, University of Cambridge

2. Janelia Research Campus, Howard Hughes Medical Institute

3. MRC Laboratory of Molecular Biology

4. Decision and Bayesian Computation, Neuroscience Department & Computational Biology Department, Institut Pasteur

Abstract

Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The Drosophila melanogaster larva is a tractable model system for studying the circuit basis of behaviour, but many forms of associative learning have not yet been demonstrated in this animal. Here, we developed a high-throughput (i.e. multi-larva) training system that combines real-time behaviour detection of freely moving larvae with targeted opto- and thermogenetic stimulation of tracked animals. Both stimuli are controlled in either open- or closed-loop, and delivered with high temporal and spatial precision. Using this tracker, we show for the first time that Drosophila larvae can perform classical conditioning with no overlap between sensory stimuli (i.e. trace conditioning). We also demonstrate that larvae are capable of operant conditioning by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Our results extend the known associative learning capacities of Drosophila larvae. Our automated training rig will facilitate the study of many different forms of associative learning and the identification of the neural circuits that underpin them.

Funder

Gates Cambridge Trust

Cambridge Commonwealth, European & International Trust

Howard Hughes Medical Institute Janelia Research Campus Visitor Scientist Program

Trinity College, University of Cambridge

Howard Hughes Medical Institute Janelia Research Campus

European Research Council

Wellcome Trust

Medical Research Council

Human Frontier Science Program

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Reference171 articles.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3