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Wu M, Liu F, Wang H, Yao L, Wei C, Zheng Q, Han J, Liu Z, Liu Y, Duan H, Ren W, Sun Z. Characterizing the dynamic learning process: Implications of a quantitative analysis. Behav Brain Res 2024; 463:114915. [PMID: 38368954 DOI: 10.1016/j.bbr.2024.114915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 02/05/2024] [Accepted: 02/15/2024] [Indexed: 02/20/2024]
Abstract
Understanding the neural mechanisms involved in learning processes is crucial for unraveling the complexities of behavior and cognition. Sudden change from the untrained level to the fully-learned level is a pivotal feature of instrumental learning. However, the concept of change point and suitable methods to conveniently analyze the characteristics of sudden change in groups remain elusive, which might hinder a fuller understanding of the neural mechanism underlying dynamic leaning process. In the current study, we investigated the learning processes of mice that were trained in an aversive instrumental learning task, and introduced a novel strategy to analyze behavioral variations in instrumental learning, leading to improved clarity on the concept of sudden change and enabling comprehensive group analysis. By applying this novel strategy, we examined the effects of cocaine and a cannabinoid receptor agonist on instrumental learning. Intriguingly, our analysis revealed significant differences in timing and occurrence of sudden changes that were previously overlooked using traditional analysis. Overall, our research advances understanding of behavioral variation during instrumental learning and the interplay between learning behaviors and neurotransmitter systems, contributing to a deeper comprehension of learning processes and informing future investigations and therapeutic interventions.
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Affiliation(s)
- Meilin Wu
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Fuhong Liu
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Hao Wang
- College of Life Sciences, Shaanxi Normal University, Xi'an 710062, China
| | - Li Yao
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Chunling Wei
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Qiaohua Zheng
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Jing Han
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Zhiqiang Liu
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Yihui Liu
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Haijun Duan
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an 710062, China
| | - Wei Ren
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an 710062, China; Faculty of Education, Shaanxi Normal University, Xi'an 710062, China.
| | - Zongpeng Sun
- School of Psychology, Shaanxi Normal University, Xi'an 710062, China.
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2
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Jürgensen AM, Sakagiannis P, Schleyer M, Gerber B, Nawrot MP. Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva. iScience 2024; 27:108640. [PMID: 38292165 PMCID: PMC10824792 DOI: 10.1016/j.isci.2023.108640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 11/10/2023] [Accepted: 12/01/2023] [Indexed: 02/01/2024] Open
Abstract
Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between cues and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of the Drosophila larva mushroom body. It includes a feedback motif conveying learned reinforcement expectation to dopaminergic neurons, which can compute prediction error as the difference between expected and present reinforcement. We demonstrate that this can serve as a driving force in learning. When combined with synaptic homeostasis, our model accounts for theoretically derived features of acquisition and loss of associations that depend on the intensity of the reinforcement and its temporal proximity to the cue. From modeling olfactory learning over the time course of behavioral experiments and simulating the locomotion of individual larvae toward or away from odor sources in a virtual environment, we conclude that learning driven by prediction errors can explain larval behavior.
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Affiliation(s)
- Anna-Maria Jürgensen
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
| | - Panagiotis Sakagiannis
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
| | - Michael Schleyer
- Leibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany
- Institute for the Advancement of Higher Education, Faculty of Science, Hokkaido University, Sapporo 060-08080, Japan
| | - Bertram Gerber
- Leibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany
- Institute for Biology, Otto-von-Guericke University, 39120 Magdeburg, Germany
- Center for Brain and Behavioral Sciences (CBBS), Otto-von-Guericke University, 39118 Magdeburg, Germany
| | - Martin Paul Nawrot
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
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3
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Ali MZ, Anushree A, Bilgrami AL, Ahsan A, Ola MS, Haque R, Ahsan J. Phenylacetaldehyde induced olfactory conditioning in Drosophila melanogaster (Diptera: Drosophilidae) larvae. JOURNAL OF INSECT SCIENCE (ONLINE) 2023; 23:25. [PMID: 38092368 PMCID: PMC10718815 DOI: 10.1093/jisesa/iead112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 11/09/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023]
Abstract
Phenylacetaldehyde (PAH), an aromatic odorant, exists in varied fruits including overripe bananas and prickly pear cactus, the 2 major host fruits of Drosophila melanogaster. It acts as a potent ligand for the Ionotropic receptor 84a (IR84a) and the Odorant receptor 67a (OR67a), serving as an important food and courtship cue for adult fruit flies. Drosophila melanogaster larvae respond robustly to diverse feeding odorants, such as ethyl acetate (EA), an aliphatic ester. Since the chemical identity and concentration of an odorant are vital neural information handled by the olfactory system, we studied how larvae respond to PAH, an aromatic food odorant with aphrodisiac properties for adult flies. Our findings revealed that PAH attracted larvae significantly in a dose-dependent manner. Larvae could also be trained with PAH associated to appetitive and aversive reinforcers. Thus, like EA, PAH might serve as an important odorant cue for larvae, aiding in food tracking and survival in the wild. Since IR84a/IR8a complex primarily governs PAH response in adult flies, we examined expression of Ir84a and Ir8a in early third-instar larvae. Our experiments showed the presence of Ir8a, a novel finding. However, contrary to adult flies, PAH-responsive Ir84a was not found. Our behavioral experiments with Ir8a1 mutant larvae exhibited normal chemotaxis to PAH, whereas Orco1 mutant showed markedly reduced chemotaxis, indicating an OR-mediated neural circuitry for sensing of PAH in larvae. The results obtained through this study are significantly important as information on how larvae perceive and process PAH odorant at the neuronal level is lacking.
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Affiliation(s)
- Md Zeeshan Ali
- Department of Biotechnology, Central University of South Bihar, Gaya, Bihar, India
| | - Anushree Anushree
- Department of Biotechnology, Central University of South Bihar, Gaya, Bihar, India
| | - Anwar L Bilgrami
- Deanship of Scientific Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Aarif Ahsan
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Mohammad Shamsul Ola
- Department of Biochemistry, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Rizwanul Haque
- Department of Biotechnology, Central University of South Bihar, Gaya, Bihar, India
| | - Jawaid Ahsan
- Department of Biotechnology, Central University of South Bihar, Gaya, Bihar, India
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4
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Rajagopalan AE, Darshan R, Hibbard KL, Fitzgerald JE, Turner GC. Reward expectations direct learning and drive operant matching in Drosophila. Proc Natl Acad Sci U S A 2023; 120:e2221415120. [PMID: 37733736 PMCID: PMC10523640 DOI: 10.1073/pnas.2221415120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 08/11/2023] [Indexed: 09/23/2023] Open
Abstract
Foraging animals must use decision-making strategies that dynamically adapt to the changing availability of rewards in the environment. A wide diversity of animals do this by distributing their choices in proportion to the rewards received from each option, Herrnstein's operant matching law. Theoretical work suggests an elegant mechanistic explanation for this ubiquitous behavior, as operant matching follows automatically from simple synaptic plasticity rules acting within behaviorally relevant neural circuits. However, no past work has mapped operant matching onto plasticity mechanisms in the brain, leaving the biological relevance of the theory unclear. Here, we discovered operant matching in Drosophila and showed that it requires synaptic plasticity that acts in the mushroom body and incorporates the expectation of reward. We began by developing a dynamic foraging paradigm to measure choices from individual flies as they learn to associate odor cues with probabilistic rewards. We then built a model of the fly mushroom body to explain each fly's sequential choice behavior using a family of biologically realistic synaptic plasticity rules. As predicted by past theoretical work, we found that synaptic plasticity rules could explain fly matching behavior by incorporating stimulus expectations, reward expectations, or both. However, by optogenetically bypassing the representation of reward expectation, we abolished matching behavior and showed that the plasticity rule must specifically incorporate reward expectations. Altogether, these results reveal the first synapse-level mechanisms of operant matching and provide compelling evidence for the role of reward expectation signals in the fly brain.
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Affiliation(s)
- Adithya E. Rajagopalan
- Janelia Research Campus, HHMI, Ashburn, VA20147
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD21205
| | - Ran Darshan
- Janelia Research Campus, HHMI, Ashburn, VA20147
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Sagol School of Neuroscience, The School of Physics and Astronomy, Tel Aviv University, Tel Aviv6997801, Israel
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Davis RL. Learning and memory using Drosophila melanogaster: a focus on advances made in the fifth decade of research. Genetics 2023; 224:iyad085. [PMID: 37212449 PMCID: PMC10411608 DOI: 10.1093/genetics/iyad085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/03/2023] [Indexed: 05/23/2023] Open
Abstract
In the last decade, researchers using Drosophila melanogaster have made extraordinary progress in uncovering the mysteries underlying learning and memory. This progress has been propelled by the amazing toolkit available that affords combined behavioral, molecular, electrophysiological, and systems neuroscience approaches. The arduous reconstruction of electron microscopic images resulted in a first-generation connectome of the adult and larval brain, revealing complex structural interconnections between memory-related neurons. This serves as substrate for future investigations on these connections and for building complete circuits from sensory cue detection to changes in motor behavior. Mushroom body output neurons (MBOn) were discovered, which individually forward information from discrete and non-overlapping compartments of the axons of mushroom body neurons (MBn). These neurons mirror the previously discovered tiling of mushroom body axons by inputs from dopamine neurons and have led to a model that ascribes the valence of the learning event, either appetitive or aversive, to the activity of different populations of dopamine neurons and the balance of MBOn activity in promoting avoidance or approach behavior. Studies of the calyx, which houses the MBn dendrites, have revealed a beautiful microglomeruluar organization and structural changes of synapses that occur with long-term memory (LTM) formation. Larval learning has advanced, positioning it to possibly lead in producing new conceptual insights due to its markedly simpler structure over the adult brain. Advances were made in how cAMP response element-binding protein interacts with protein kinases and other transcription factors to promote the formation of LTM. New insights were made on Orb2, a prion-like protein that forms oligomers to enhance synaptic protein synthesis required for LTM formation. Finally, Drosophila research has pioneered our understanding of the mechanisms that mediate permanent and transient active forgetting, an important function of the brain along with acquisition, consolidation, and retrieval. This was catalyzed partly by the identification of memory suppressor genes-genes whose normal function is to limit memory formation.
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Affiliation(s)
- Ronald L Davis
- Department of Neuroscience, Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, University of Florida, 130 Scripps Way, Jupiter, FL 33458, USA
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6
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Thane M, Paisios E, Stöter T, Krüger AR, Gläß S, Dahse AK, Scholz N, Gerber B, Lehmann DJ, Schleyer M. High-resolution analysis of individual Drosophila melanogaster larvae uncovers individual variability in locomotion and its neurogenetic modulation. Open Biol 2023; 13:220308. [PMID: 37072034 PMCID: PMC10113034 DOI: 10.1098/rsob.220308] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/05/2023] [Indexed: 04/20/2023] Open
Abstract
Neuronally orchestrated muscular movement and locomotion are defining faculties of multicellular animals. Due to its simple brain and genetic accessibility, the larva of the fruit fly Drosophila melanogaster allows one to study these processes at tractable levels of complexity. However, although the faculty of locomotion clearly pertains to the individual, most studies of locomotion in larvae use measurements aggregated across animals, or animals tested one by one, an extravagance for larger-scale analyses. This prevents grasping the inter- and intra-individual variability in locomotion and its neurogenetic determinants. Here, we present the IMBA (individual maggot behaviour analyser) for analysing the behaviour of individual larvae within groups, reliably resolving individual identity across collisions. We use the IMBA to systematically describe the inter- and intra-individual variability in locomotion of wild-type animals, and how the variability is reduced by associative learning. We then report a novel locomotion phenotype of an adhesion GPCR mutant. We further investigated the modulation of locomotion across repeated activations of dopamine neurons in individual animals, and the transient backward locomotion induced by brief optogenetic activation of the brain-descending 'mooncrawler' neurons. In summary, the IMBA is an easy-to-use toolbox allowing an unprecedentedly rich view of the behaviour and its variability of individual larvae, with utility in multiple biomedical research contexts.
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Affiliation(s)
- Michael Thane
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Simulation and Graphics, Otto von Guerike University, Magdeburg, Germany
| | - Emmanouil Paisios
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Torsten Stöter
- Combinatorial NeuroImaging Core Facility, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Anna-Rosa Krüger
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Institute of Biology, Free University of Berlin, Berlin, Germany
| | - Sebastian Gläß
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Anne-Kristin Dahse
- Division of General Biochemistry, Rudolf Schönheimer Institute of Biochemistry, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Nicole Scholz
- Division of General Biochemistry, Rudolf Schönheimer Institute of Biochemistry, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Bertram Gerber
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Institute of Biology, Otto von Guericke University Magdeburg, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Dirk J. Lehmann
- Department of Simulation and Graphics, Otto von Guerike University, Magdeburg, Germany
- Department for Information Engineering, Faculty of Computer Science, Ostfalia University of Applied Science, Brunswick-Wolfenbuettel, Germany
| | - Michael Schleyer
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
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7
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Winding M, Pedigo BD, Barnes CL, Patsolic HG, Park Y, Kazimiers T, Fushiki A, Andrade IV, Khandelwal A, Valdes-Aleman J, Li F, Randel N, Barsotti E, Correia A, Fetter RD, Hartenstein V, Priebe CE, Vogelstein JT, Cardona A, Zlatic M. The connectome of an insect brain. Science 2023; 379:eadd9330. [PMID: 36893230 PMCID: PMC7614541 DOI: 10.1126/science.add9330] [Citation(s) in RCA: 82] [Impact Index Per Article: 82.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 02/07/2023] [Indexed: 03/11/2023]
Abstract
Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.
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Affiliation(s)
- Michael Winding
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Benjamin D. Pedigo
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
| | - Christopher L. Barnes
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Heather G. Patsolic
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Accenture, Arlington, VA, USA
| | - Youngser Park
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Tom Kazimiers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- kazmos GmbH, Dresden, Germany
| | - Akira Fushiki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ingrid V. Andrade
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Avinash Khandelwal
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Javier Valdes-Aleman
- University of Cambridge, Department of Zoology, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Nadine Randel
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
| | - Elizabeth Barsotti
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Ana Correia
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Richard D. Fetter
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Stanford University, Stanford, CA, USA
| | - Volker Hartenstein
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Carey E. Priebe
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Joshua T. Vogelstein
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Albert Cardona
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Marta Zlatic
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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8
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Yu J, Dancausse S, Paz M, Faderin T, Gaviria M, Shomar J, Zucker D, Venkatachalam V, Klein M. Continuous, long-term crawling behavior characterized by a robotic transport system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.27.530235. [PMID: 36909608 PMCID: PMC10002653 DOI: 10.1101/2023.02.27.530235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Detailed descriptions of behavior provide critical insight into the structure and function of nervous systems. In Drosophila larvae and many other systems, short behavioral experiments have been successful in characterizing rapid responses to a range of stimuli at the population level. However, the lack of long-term continuous observation makes it difficult to dissect comprehensive behavioral dynamics of individual animals and how behavior (and therefore the nervous system) develops over time. To allow for long-term continuous observations in individual fly larvae, we have engineered a robotic instrument that automatically tracks and transports larvae throughout an arena. The flexibility and reliability of its design enables controlled stimulus delivery and continuous measurement over developmental time scales, yielding an unprecedented level of detailed locomotion data. We utilize the new system’s capabilities to perform continuous observation of exploratory behavior over a duration of six hours with and without a thermal gradient present, and in a single larva for over 30 hours. Long-term free-roaming behavior and analogous short-term experiments show similar dynamics that take place at the beginning of each experiment. Finally, characterization of larval thermotaxis in individuals reveals a bimodal distribution in navigation efficiency, identifying distinct phenotypes that are obfuscated when only analyzing population averages.
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Honda T. Optogenetic and thermogenetic manipulation of defined neural circuits and behaviors in Drosophila. Learn Mem 2022; 29:100-109. [PMID: 35332066 PMCID: PMC8973390 DOI: 10.1101/lm.053556.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/06/2022] [Indexed: 11/25/2022]
Abstract
Neural network dynamics underlying flexible animal behaviors remain elusive. The fruit fly Drosophila melanogaster is considered an excellent model in behavioral neuroscience because of its simple neuroanatomical architecture and the availability of various genetic methods. Moreover, Drosophila larvae's transparent body allows investigators to use optical methods on freely moving animals, broadening research directions. Activating or inhibiting well-defined events in excitable cells with a fine temporal resolution using optogenetics and thermogenetics led to the association of functions of defined neural populations with specific behavioral outputs such as the induction of associative memory. Furthermore, combining optogenetics and thermogenetics with state-of-the-art approaches, including connectome mapping and machine learning-based behavioral quantification, might provide a complete view of the experience- and time-dependent variations of behavioral responses. These methodologies allow further understanding of the functional connections between neural circuits and behaviors such as chemosensory, motivational, courtship, and feeding behaviors and sleep, learning, and memory.
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Affiliation(s)
- Takato Honda
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
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10
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Smith MAY, Honegger KS, Turner G, de Bivort B. Idiosyncratic learning performance in flies. Biol Lett 2022; 18:20210424. [PMID: 35104427 PMCID: PMC8807056 DOI: 10.1098/rsbl.2021.0424] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 12/21/2021] [Indexed: 11/18/2022] Open
Abstract
Individuals vary in their innate behaviours, even when they have the same genome and have been reared in the same environment. The extent of individuality in plastic behaviours, like learning, is less well characterized. Also unknown is the extent to which intragenotypic differences in learning generalize: if an individual performs well in one assay, will it perform well in other assays? We investigated this using the fruit fly Drosophila melanogaster, an organism long-used to study the mechanistic basis of learning and memory. We found that isogenic flies, reared in identical laboratory conditions, and subject to classical conditioning that associated odorants with electric shock, exhibit clear individuality in their learning responses. Flies that performed well when an odour was paired with shock tended to perform well when the odour was paired with bitter taste or when other odours were paired with shock. Thus, individuality in learning performance appears to be prominent in isogenic animals reared identically, and individual differences in learning performance generalize across some aversive sensory modalities. Establishing these results in flies opens up the possibility of studying the genetic and neural circuit basis of individual differences in learning in a highly suitable model organism.
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Affiliation(s)
- Matthew A.-Y. Smith
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Kyle S. Honegger
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60611, USA
| | - Glenn Turner
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Benjamin de Bivort
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
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11
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Lesar A, Tahir J, Wolk J, Gershow M. Switch-like and persistent memory formation in individual Drosophila larvae. eLife 2021; 10:e70317. [PMID: 34636720 PMCID: PMC8510578 DOI: 10.7554/elife.70317] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 08/27/2021] [Indexed: 11/15/2022] Open
Abstract
Associative learning allows animals to use past experience to predict future events. The circuits underlying memory formation support immediate and sustained changes in function, often in response to a single example. Larval Drosophila is a genetic model for memory formation that can be accessed at molecular, synaptic, cellular, and circuit levels, often simultaneously, but existing behavioral assays for larval learning and memory do not address individual animals, and it has been difficult to form long-lasting memories, especially those requiring synaptic reorganization. We demonstrate a new assay for learning and memory capable of tracking the changing preferences of individual larvae. We use this assay to explore how activation of a pair of reward neurons changes the response to the innately aversive gas carbon dioxide (CO2). We confirm that when coupled to CO2 presentation in appropriate temporal sequence, optogenetic reward reduces avoidance of CO2. We find that learning is switch-like: all-or-none and quantized in two states. Memories can be extinguished by repeated unrewarded exposure to CO2 but are stabilized against extinction by repeated training or overnight consolidation. Finally, we demonstrate long-lasting protein synthesis dependent and independent memory formation.
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Affiliation(s)
- Amanda Lesar
- Department of Physics, New York UniversityNew YorkUnited States
| | - Javan Tahir
- Department of Physics, New York UniversityNew YorkUnited States
| | - Jason Wolk
- Department of Physics, New York UniversityNew YorkUnited States
| | - Marc Gershow
- Department of Physics, New York UniversityNew YorkUnited States
- Center for Neural Science, New York UniversityNew YorkUnited States
- NYU Neuroscience Institute, New York University Langone Medical CenterNew YorkUnited States
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