Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794639
Title: High-throughput operant conditioning in Drosophila larvae
Author: Klein, Kristina Tenna
ISNI:       0000 0004 8500 4229
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2020
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Abstract:
Operant conditioning is the process by which animals learn to associate their own behaviour with positive or negative outcomes, biasing future action selection in order to maximise reward and avoid punishment. It is an important strategy to ensure survival in an ever-changing environment. Although operant conditioning has been observed across vertebrate and invertebrate species, the underlying neural mechanisms are still not fully understood. The Drosophila larva is an excellent model system to study neural circuits, since it is genetically tractable, with a variety of tools available. Although it is quite small, it is capable of a diverse range of behaviours and can achieve complex learning tasks. However, while the mechanisms underlying classical conditioning, where animals learn about the appetitive or aversive qualities of an external sensory cue, have been extensively studied in larvae, it has remained an open question whether they are capable of operant conditioning. This is in part due to the challenges which arise during the training process: in order to train an animal to associate its own actions with their outcomes, the experimenter needs to be able to deliver rewarding or punishing stimuli directly in response to behaviour. In this thesis, I introduce a novel high-throughput tracker suitable for training up to 16 larvae simultaneously. I have developed a customised software for real-time detection of various actions that larvae perform: left and right bend, forward crawl, roll and back-up. Light and heat stimuli can be administered at individual animals with minimal delay, enabling optogenetic or thermogenetic activation of circuits encoding reward or punishment in response to behaviour. Using this system, I show that Drosophila larvae are capable of operant conditioning. Pairing bends to one direction, e.g. the left, with optogenetic activation of a large group of reward-encoding dopaminergic and serotonergic neurons is sufficient to induce a learned preference for bending towards this side after training. I explore whether there are other types of actions which larvae can learn to associate with valence, and introduce a second operant conditioning paradigm, in which larvae modify their behaviour following pairing of the stimulus with forward crawls. To identify new candidate neurons signalling valence in a learning context, I also conduct a classical conditioning screen, in which I pair an odour with optogenetic activation of distinct neuron types covered by different driver lines. While activation of many types of gustatory sensory neurons paired with the odour was insufficient for memory formation, I find that the serotonergic neurons of the brain and the subesophageal zone (SEZ) can induce strong appetitive learning. Finally, I show that activity of serotonergic rather than dopaminergic neurons is sufficient for memory formation in the operant bend direction paradigm, and that operant conditioning is impaired when restricting activation to the serotonergic neurons of the brain and the SEZ. My results suggest a novel role of serotonergic neurons for learning in insects as well as the existence of learning circuits outside of the mushroom body. Different subsets of serotonergic neurons mediate classical and operant conditioning. This works lays a foundation for future studies of the function of serotonin and the mechanisms underlying operant conditioning at both circuit level and cellular level.
Supervisor: Zlatic, Marta Sponsor: Gates Cambridge Scholarship
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.794639  DOI:
Keywords: Drosophila ; larva ; operant ; learning ; conditioning ; associative learning ; operant conditioning ; operant learning ; high-throughput ; tracker ; tracking ; machine learning ; computer vision ; neuroscience ; circuit ; behaviour ; detection ; behaviour detection ; behavior ; real-time ; FPGA ; serotonin ; serotonergic ; dopamine ; DDC ; classical conditioning ; Pavlovian
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