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Traniello IM, Kocher SD. Integrating computer vision and molecular neurobiology to bridge the gap between behavior and the brain. CURRENT OPINION IN INSECT SCIENCE 2024:101259. [PMID: 39244088 DOI: 10.1016/j.cois.2024.101259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/23/2024] [Accepted: 09/02/2024] [Indexed: 09/09/2024]
Abstract
The past decade of social insect research has seen rapid development in automated behavioral tracking and molecular profiling of the nervous system, two distinct but complementary lines of inquiry into phenotypic variation across individuals, colonies, populations, and species. These experimental strategies have developed largely in parallel, as automated tracking generates a continuous stream of behavioral data while, in contrast, 'omics-based profiling provides a single "snapshot" of the brain. Better integration of these approaches applied to studying variation in social behavior will reveal the underlying genetic and neurobiological mechanisms that shape the evolution and diversification of social life. In this review, we discuss relevant advances in both fields and propose new strategies to better elucidate the molecular and behavioral innovations that generate social life.
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Affiliation(s)
- Ian M Traniello
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ; Department of Ecology and Evolutionary Biology, Princeton University
| | - Sarah D Kocher
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ; Department of Ecology and Evolutionary Biology, Princeton University
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2
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Sridhar G, Vergassola M, Marques JC, Orger MB, Costa AC, Wyart C. Uncovering multiscale structure in the variability of larval zebrafish navigation. ARXIV 2024:arXiv:2405.17143v1. [PMID: 38855549 PMCID: PMC11160889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Animals chain movements into long-lived motor strategies, exhibiting variability across scales that reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build Markov models of movement sequences that bridges across time scales and enables a quantitative comparison of behavioral phenotypes among individuals. Applied to larval zebrafish responding to diverse sensory cues, we uncover a hierarchy of long-lived motor strategies, dominated by changes in orientation distinguishing cruising versus wandering strategies. Environmental cues induce preferences along these modes at the population level: while fish cruise in the light, they wander in response to aversive stimuli, or in search for appetitive prey. As our method encodes the behavioral dynamics of each individual fish in the transitions among coarse-grained motor strategies, we use it to uncover a hierarchical structure in the phenotypic variability that reflects exploration-exploitation trade-offs. Across a wide range of sensory cues, a major source of variation among fish is driven by prior and/or immediate exposure to prey that induces exploitation phenotypes. A large degree of variability that is not explained by environmental cues unravels motivational states that override the sensory context to induce contrasting exploration-exploitation phenotypes. Altogether, by extracting the timescales of motor strategies deployed during navigation, our approach exposes structure among individuals and reveals internal states tuned by prior experience.
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Affiliation(s)
- Gautam Sridhar
- Sorbonne University, Paris Brain Institute (ICM), Inserm U1127, CNRS UMR 7225, Paris, France
| | - Massimo Vergassola
- Laboratoire de Physique de l’Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - João C. Marques
- Champalimaud Research, Champalimaud Centre for the Unknown, Avenida Brasília, Doca de Pedrouços, Lisboa 1400-038, Portugal
| | - Michael B. Orger
- Champalimaud Research, Champalimaud Centre for the Unknown, Avenida Brasília, Doca de Pedrouços, Lisboa 1400-038, Portugal
| | - Antonio Carlos Costa
- Sorbonne University, Paris Brain Institute (ICM), Inserm U1127, CNRS UMR 7225, Paris, France
- Laboratoire de Physique de l’Ecole normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Claire Wyart
- Sorbonne University, Paris Brain Institute (ICM), Inserm U1127, CNRS UMR 7225, Paris, France
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3
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Sridhar G, Vergassola M, Marques JC, Orger MB, Costa AC, Wyart C. Uncovering multiscale structure in the variability of larval zebrafish navigation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.16.594521. [PMID: 38798455 PMCID: PMC11118365 DOI: 10.1101/2024.05.16.594521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Animals chain movements into long-lived motor strategies, resulting in variability that ultimately reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build models that bridges across time scales that enable a quantitative comparison of behavioral phenotypes among individuals. Applied to larval zebrafish exposed to diverse sensory cues, we uncover a hierarchy of long-lived motor strategies, dominated by changes in orientation distinguishing cruising and wandering strategies. Environmental cues induce preferences along these modes at the population level: while fish cruise in the light, they wander in response to aversive (dark) stimuli or in search for prey. Our method enables us to encode the behavioral dynamics of each individual fish in the transitions among coarse-grained motor strategies. By doing so, we uncover a hierarchical structure to the phenotypic variability that corresponds to exploration-exploitation trade-offs. Within a wide range of sensory cues, a major source of variation among fish is driven by prior and immediate exposure to prey that induces exploitation phenotypes. However, a large degree of variability is unexplained by environmental cues, pointing to hidden states that override the sensory context to induce contrasting exploration-exploitation phenotypes. Altogether, our approach extracts the timescales of motor strategies deployed during navigation, exposing undiscovered structure among individuals and pointing to internal states tuned by prior experience.
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4
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Zhang C, Ji L, Li Z, Lucas JR, Feng J, Sun C, Jiang T. Resting posture drives the evolution of agonistic displays in bats. Evolution 2024; 78:964-970. [PMID: 38305496 DOI: 10.1093/evolut/qpae019] [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: 12/12/2023] [Accepted: 02/01/2024] [Indexed: 02/03/2024]
Abstract
Agonistic displays are one of the most diverse social behaviors that have important functions in animal's life history. However, their origin and driving factors have largely been unexplored. Here, we evaluated agonistic displays of 71 bat species across 10 families and classified these displays into two categories: (a) boxing displays where a bat attacks its opponent with its wrist and thumb and (b) pushing displays where a bat uses its head or body to hit a rival. We estimated the strength of the phylogenetic signal of the agonistic displays, revealed their origin, and tested the potential evolutionary relationships between agonistic behaviors and body size or resting posture (free hanging vs. contact hanging where the bat is in contact with some surface). We found that agonistic displays were phylogenetically conserved and that boxing displays are the ancestral state. Moreover, we found that bats with a free-hanging resting posture were more likely to exhibit boxing displays than pushing displays. In addition, bats with longer forearms do not have a higher propensity for boxing displays. This study expands our limited knowledge of the evolution of agonistic displays and highlights the importance of resting posture as a driving force in the diversity of agonistic displays.
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Affiliation(s)
- Chunmian Zhang
- Hebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, China
- Hebei Collaborative Innovation Center for Eco-Environment, Hebei Normal University, Shijiazhuang, China
| | - Ligen Ji
- Hebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, China
- Hebei Collaborative Innovation Center for Eco-Environment, Hebei Normal University, Shijiazhuang, China
| | - Ziji Li
- Hebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, China
- Hebei Collaborative Innovation Center for Eco-Environment, Hebei Normal University, Shijiazhuang, China
| | - Jeffrey R Lucas
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
| | - Jiang Feng
- Jilin Provincial Key Laboratory of Animal Resource Conservation and Utilization, Northeast Normal University, Changchun, China
- College of Life Science, Jilin Agricultural University, Changchun, China
| | - Congnan Sun
- Hebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, China
- Hebei Collaborative Innovation Center for Eco-Environment, Hebei Normal University, Shijiazhuang, China
| | - Tinglei Jiang
- Jilin Provincial Key Laboratory of Animal Resource Conservation and Utilization, Northeast Normal University, Changchun, China
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5
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Bialek W, Shaevitz JW. Long Timescales, Individual Differences, and Scale Invariance in Animal Behavior. PHYSICAL REVIEW LETTERS 2024; 132:048401. [PMID: 38335334 DOI: 10.1103/physrevlett.132.048401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 11/27/2023] [Indexed: 02/12/2024]
Abstract
The explosion of data on animal behavior in more natural contexts highlights the fact that these behaviors exhibit correlations across many timescales. However, there are major challenges in analyzing these data: records of behavior in single animals have fewer independent samples than one might expect. In pooling data from multiple animals, individual differences can mimic long-ranged temporal correlations; conversely, long-ranged correlations can lead to an overestimate of individual differences. We suggest an analysis scheme that addresses these problems directly, apply this approach to data on the spontaneous behavior of walking flies, and find evidence for scale-invariant correlations over nearly three decades in time, from seconds to one hour. Three different measures of correlation are consistent with a single underlying scaling field of dimension Δ=0.180±0.005.
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Affiliation(s)
- William Bialek
- Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
- Center for Studies in Physics and Biology, Rockefeller University, New York, New York 10065, USA
| | - Joshua W Shaevitz
- Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
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6
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Wolf SW, Ruttenberg DM, Knapp DY, Webb AE, Traniello IM, McKenzie-Smith GC, Leheny SA, Shaevitz JW, Kocher SD. NAPS: Integrating pose estimation and tag-based tracking. Methods Ecol Evol 2023; 14:2541-2548. [PMID: 38681746 PMCID: PMC11052584 DOI: 10.1111/2041-210x.14201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 08/02/2023] [Indexed: 05/01/2024]
Abstract
1. Significant advances in computational ethology have allowed the quantification of behaviour in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both. 2. To capture finely resolved behaviours while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state-of-the-art, deep learning-based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens). 3. We provide a stand-alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioural variation within individuals in a group. 4. Expanding the toolkit for capturing the constituent behaviours of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviours and can provide critical data for understanding how individual variation influences collective dynamics.
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Affiliation(s)
- Scott W. Wolf
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
| | - Dee M. Ruttenberg
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
| | - Daniel Y. Knapp
- Department of Physics, Princeton University, Princeton, New Jersey, USA
| | - Andrew E. Webb
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
| | - Ian M. Traniello
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
| | | | - Sophie A. Leheny
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Joshua W. Shaevitz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
- Department of Physics, Princeton University, Princeton, New Jersey, USA
| | - Sarah D. Kocher
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
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7
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Couzin ID, Heins C. Emerging technologies for behavioral research in changing environments. Trends Ecol Evol 2023; 38:346-354. [PMID: 36509561 DOI: 10.1016/j.tree.2022.11.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/14/2022] [Accepted: 11/21/2022] [Indexed: 12/13/2022]
Abstract
The first response exhibited by animals to changing environments is typically behavioral. Behavior is thus central to predicting, and mitigating, the impacts that natural and anthropogenic environmental changes will have on populations and, consequently, ecosystems. Yet the inherently multiscale nature of behavior, as well as the complexities associated with inferring how animals perceive their world, and make decisions, has constrained the scope of behavioral research. Major technological advances in electronics and in machine learning, however, provide increasingly powerful means to see, analyze, and interpret behavior in its natural complexity. We argue that these disruptive technologies will foster new approaches that will allow us to move beyond quantitative descriptions and reveal the underlying generative processes that give rise to behavior.
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Affiliation(s)
- Iain D Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour & Department of Biology, University of Konstanz, Germany.
| | - Conor Heins
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour & Department of Biology, University of Konstanz, Germany
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8
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Ehlman SM, Scherer U, Bierbach D, Francisco FA, Laskowski KL, Krause J, Wolf M. Leveraging big data to uncover the eco-evolutionary factors shaping behavioural development. Proc Biol Sci 2023; 290:20222115. [PMID: 36722081 PMCID: PMC9890127 DOI: 10.1098/rspb.2022.2115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Mapping the eco-evolutionary factors shaping the development of animals' behavioural phenotypes remains a great challenge. Recent advances in 'big behavioural data' research-the high-resolution tracking of individuals and the harnessing of that data with powerful analytical tools-have vastly improved our ability to measure and model developing behavioural phenotypes. Applied to the study of behavioural ontogeny, the unfolding of whole behavioural repertoires can be mapped in unprecedented detail with relative ease. This overcomes long-standing experimental bottlenecks and heralds a surge of studies that more finely define and explore behavioural-experiential trajectories across development. In this review, we first provide a brief guide to state-of-the-art approaches that allow the collection and analysis of high-resolution behavioural data across development. We then outline how such approaches can be used to address key issues regarding the ecological and evolutionary factors shaping behavioural development: developmental feedbacks between behaviour and underlying states, early life effects and behavioural transitions, and information integration across development.
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Affiliation(s)
- Sean M. Ehlman
- SCIoI Excellence Cluster, 10587 Berlin, Germany,Faculty of Life Sciences, Humboldt University, 10117 Berlin, Germany,Department of Fish Biology, Fisheries, and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, 12587 Berlin, Germany
| | - Ulrike Scherer
- SCIoI Excellence Cluster, 10587 Berlin, Germany,Faculty of Life Sciences, Humboldt University, 10117 Berlin, Germany,Department of Fish Biology, Fisheries, and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, 12587 Berlin, Germany
| | - David Bierbach
- SCIoI Excellence Cluster, 10587 Berlin, Germany,Faculty of Life Sciences, Humboldt University, 10117 Berlin, Germany,Department of Fish Biology, Fisheries, and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, 12587 Berlin, Germany
| | - Fritz A. Francisco
- SCIoI Excellence Cluster, 10587 Berlin, Germany,Faculty of Life Sciences, Humboldt University, 10117 Berlin, Germany
| | - Kate L. Laskowski
- Department of Evolution and Ecology, University of California – Davis, Davis, CA 95616, USA
| | - Jens Krause
- SCIoI Excellence Cluster, 10587 Berlin, Germany,Faculty of Life Sciences, Humboldt University, 10117 Berlin, Germany,Department of Fish Biology, Fisheries, and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, 12587 Berlin, Germany
| | - Max Wolf
- SCIoI Excellence Cluster, 10587 Berlin, Germany,Department of Fish Biology, Fisheries, and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, 12587 Berlin, Germany
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9
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York RA, Brezovec LE, Coughlan J, Herbst S, Krieger A, Lee SY, Pratt B, Smart AD, Song E, Suvorov A, Matute DR, Tuthill JC, Clandinin TR. The evolutionary trajectory of drosophilid walking. Curr Biol 2022; 32:3005-3015.e6. [PMID: 35671756 PMCID: PMC9329251 DOI: 10.1016/j.cub.2022.05.039] [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: 12/06/2021] [Revised: 03/03/2022] [Accepted: 05/13/2022] [Indexed: 11/26/2022]
Abstract
Neural circuits must both execute the behavioral repertoire of individuals and account for behavioral variation across species. Understanding how this variation emerges over evolutionary time requires large-scale phylogenetic comparisons of behavioral repertoires. Here, we describe the evolution of walking in fruit flies by capturing high-resolution, unconstrained movement from 13 species and 15 strains of drosophilids. We find that walking can be captured in a universal behavior space, the structure of which is evolutionarily conserved. However, the occurrence of and transitions between specific movements have evolved rapidly, resulting in repeated convergent evolution in the temporal structure of locomotion. Moreover, a meta-analysis demonstrates that many behaviors evolve more rapidly than other traits. Thus, the architecture and physiology of locomotor circuits can execute precise individual movements in one species and simultaneously support rapid evolutionary changes in the temporal ordering of these modular elements across clades.
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Affiliation(s)
- Ryan A York
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA.
| | - Luke E Brezovec
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Jenn Coughlan
- Biology Department, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Steven Herbst
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Avery Krieger
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Su-Yee Lee
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Brandon Pratt
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Ashley D Smart
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Eugene Song
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Anton Suvorov
- Biology Department, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Daniel R Matute
- Biology Department, University of North Carolina, Chapel Hill, NC 27599, USA
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA.
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10
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Kennedy A. The what, how, and why of naturalistic behavior. Curr Opin Neurobiol 2022; 74:102549. [PMID: 35537373 PMCID: PMC9273162 DOI: 10.1016/j.conb.2022.102549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/23/2022] [Accepted: 03/28/2022] [Indexed: 01/03/2023]
Abstract
In the past few years, advances in machine learning have fueled an explosive growth of descriptive and generative models of animal behavior. These new approaches offer higher levels of detail and granularity than has previously been possible, allowing for fine-grained segmentation of animals' actions and precise quantitative mappings between an animal's sensory environment and its behavior. How can these new methods help us understand the governing principles shaping complex and naturalistic behavior? In this review, we will recap ways in which our ability to detect and model behavior have improved in recent years, and consider how these techniques might be used to revisit classical normative theories of behavioral control.
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Affiliation(s)
- Ann Kennedy
- Department of Neuroscience, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
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11
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Overman KE, Choi DM, Leung K, Shaevitz JW, Berman GJ. Measuring the repertoire of age-related behavioral changes in Drosophila melanogaster. PLoS Comput Biol 2022; 18:e1009867. [PMID: 35202388 PMCID: PMC8903287 DOI: 10.1371/journal.pcbi.1009867] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 03/08/2022] [Accepted: 01/25/2022] [Indexed: 11/18/2022] Open
Abstract
Aging affects almost all aspects of an organism—its morphology, its physiology, its behavior. Isolating which biological mechanisms are regulating these changes, however, has proven difficult, potentially due to our inability to characterize the full repertoire of an animal’s behavior across the lifespan. Using data from fruit flies (D. melanogaster) we measure the full repertoire of behaviors as a function of age. We observe a sexually dimorphic pattern of changes in the behavioral repertoire during aging. Although the stereotypy of the behaviors and the complexity of the repertoire overall remains relatively unchanged, we find evidence that the observed alterations in behavior can be explained by changing the fly’s overall energy budget, suggesting potential connections between metabolism, aging, and behavior. Aging is a ubiquitous biological phenomenon that affects many aspects of an animal’s appearance, physiology, and behavior. Our understanding of how changes in physiology lead to behavioral changes, however, has been partially limited by our ability to robustly quantify how behavior alters over timescales of days and weeks. In this study, we measure a large repertoire of behaviors of fruit flies at various ages, finding how the actions the animals perform shift with age. We observe a difference between the aging dynamics of male and female flies, and we show that many of these changes can be explained with a model of energy consumption, leading us to make predictions as to the role of metabolism in changes in aging behavior.
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Affiliation(s)
- Katherine E. Overman
- Department of Physics, Emory University, Atlanta, Georgia, United States of America
| | - Daniel M. Choi
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Kawai Leung
- Department of Physics, Emory University, Atlanta, Georgia, United States of America
| | - Joshua W. Shaevitz
- Department of Physics and Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, United States of America
| | - Gordon J. Berman
- Department of Physics, Emory University, Atlanta, Georgia, United States of America
- Department of Biology, Emory University, Atlanta, Georgia, United States of America
- * E-mail:
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12
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Sun JJ, Karigo T, Chakraborty D, Mohanty SP, Wild B, Sun Q, Chen C, Anderson DJ, Perona P, Yue Y, Kennedy A. The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 2021; 2021:1-15. [PMID: 38706835 PMCID: PMC11067713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists of trajectory data of social interactions, recorded from videos of freely behaving mice in a standard resident-intruder assay. To help accelerate behavioral studies, the CalMS21 dataset provides benchmarks to evaluate the performance of automated behavior classification methods in three settings: (1) for training on large behavioral datasets all annotated by a single annotator, (2) for style transfer to learn inter-annotator differences in behavior definitions, and (3) for learning of new behaviors of interest given limited training data. The dataset consists of 6 million frames of unlabeled tracked poses of interacting mice, as well as over 1 million frames with tracked poses and corresponding frame-level behavior annotations. The challenge of our dataset is to be able to classify behaviors accurately using both labeled and unlabeled tracking data, as well as being able to generalize to new settings.
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13
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Werkhoven Z, Bravin A, Skutt-Kakaria K, Reimers P, Pallares LF, Ayroles J, de Bivort BL. The structure of behavioral variation within a genotype. eLife 2021; 10:64988. [PMID: 34664550 PMCID: PMC8526060 DOI: 10.7554/elife.64988] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 09/14/2021] [Indexed: 12/13/2022] Open
Abstract
Individual animals vary in their behaviors. This is true even when they share the same genotype and were reared in the same environment. Clusters of covarying behaviors constitute behavioral syndromes, and an individual’s position along such axes of covariation is a representation of their personality. Despite these conceptual frameworks, the structure of behavioral covariation within a genotype is essentially uncharacterized and its mechanistic origins unknown. Passing hundreds of inbred Drosophila individuals through an experimental pipeline that captured hundreds of behavioral measures, we found sparse but significant correlations among small sets of behaviors. Thus, the space of behavioral variation has many independent dimensions. Manipulating the physiology of the brain, and specific neural populations, altered specific correlations. We also observed that variation in gene expression can predict an individual’s position on some behavioral axes. This work represents the first steps in understanding the biological mechanisms determining the structure of behavioral variation within a genotype.
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Affiliation(s)
- Zachary Werkhoven
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States
| | - Alyssa Bravin
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States
| | - Kyobi Skutt-Kakaria
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States.,Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
| | - Pablo Reimers
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States.,Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Luisa F Pallares
- Department of Ecology and Evolutionary Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States
| | - Julien Ayroles
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States.,Department of Ecology and Evolutionary Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States
| | - Benjamin L de Bivort
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States
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