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Ting LH, Gick B, Kesar TM, Xu J. Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230485. [PMID: 39155720 DOI: 10.1098/rstb.2023.0485] [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: 12/17/2023] [Revised: 05/15/2024] [Accepted: 06/18/2024] [Indexed: 08/20/2024] Open
Abstract
Each individual's movements are sculpted by constant interactions between sensorimotor and sociocultural factors. A theoretical framework grounded in motor control mechanisms articulating how sociocultural and biological signals converge to shape movement is currently missing. Here, we propose a framework for the emerging field of ethnokinesiology aiming to provide a conceptual space and vocabulary to help bring together researchers at this intersection. We offer a first-level schema for generating and testing hypotheses about cultural differences in movement to bridge gaps between the rich observations of cross-cultural movement variations and neurophysiological and biomechanical accounts of movement. We explicitly dissociate two interacting feedback loops that determine culturally relevant movement: one governing sensorimotor tasks regulated by neural signals internal to the body, the other governing ecological tasks generated through actions in the environment producing ecological consequences. A key idea is the emergence of individual-specific and culturally influenced motor concepts in the nervous system, low-dimensional functional mappings between sensorimotor and ecological task spaces. Motor accents arise from perceived differences in motor concept topologies across cultural contexts. We apply the framework to three examples: speech, gait and grasp. Finally, we discuss how ethnokinesiological studies may inform personalized motor skill training and rehabilitation, and challenges moving forward.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.
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Affiliation(s)
- Lena H Ting
- Coulter Department of Biomedical Engineering at Georgia Tech and Emory, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, GA 30322, USA
| | - Bryan Gick
- Department of Linguistics, The University British Columbia, Vancouver, BC V6T 1Z4, Canada
- Haskins Laboratories, Yale University, New Haven, CT 06520, USA
| | - Trisha M Kesar
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, GA 30322, USA
| | - Jing Xu
- Department of Kinesiology, The University of Georgia, Athens, GA 30602, USA
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Landaverde S, Sleep M, Lacoste A, Tan S, Schuback R, Reiter LT, Iyengar A. Glial expression of Drosophila UBE3A causes spontaneous seizures that can be modulated by 5-HT signaling. Neurobiol Dis 2024; 200:106651. [PMID: 39197537 DOI: 10.1016/j.nbd.2024.106651] [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: 04/13/2024] [Revised: 08/02/2024] [Accepted: 08/25/2024] [Indexed: 09/01/2024] Open
Abstract
Misexpression of the E3 ubiquitin ligase gene UBE3A is thought to contribute to a range of neurological disorders. In the context of Dup15q syndrome, additional genomic copies of UBE3A give rise to the autism, muscle hypotonia and spontaneous seizures characteristics of the disorder. In a Drosophila model of Dup 15q syndrome, it was recently shown that glial-driven expression of the UBE3A ortholog dube3a led to a "bang-sensitive" phenotype, where mechanical shock triggers convulsions, suggesting glial dube3a expression contributes to hyperexcitability in flies. Here we directly compare the consequences of glial- and neuronal-driven dube3a expression on motor coordination and seizure susceptibility in Drosophila. To quantify seizure-related behavioral events, we developed and trained a hidden Markov model that identified these events based on automated video tracking of fly locomotion. Both glial and neuronal driven dube3a expression led to clear motor phenotypes. However, only glial-driven dube3a expression displayed spontaneous seizure-associated immobilization events, that were clearly observed at high-temperature (38 °C). Using a tethered fly preparation amenable to electrophysiological monitoring of seizure activity, we found glial-driven dube3a flies display aberrant spontaneous spike discharges which are bilaterally synchronized. Neither neuronal-dube3a overexpressing flies, nor control flies displayed these firing patterns. We previously performed a drug screen for FDA approved compounds that can suppress bang-sensitivity in glial-driven dube3a expressing flies and identified certain 5-HT modulators as strong seizure suppressors. Here we found glial-driven dube3a flies fed the serotonin reuptake inhibitor vortioxetine and the 5-HT2A antagonist ketanserin displayed reduced immobilization and spike bursting, consistent with the previous study. Together these findings highlight the potential for glial pathophysiology to drive Dup15q syndrome-related seizure activity.
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Affiliation(s)
- Saul Landaverde
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL, United States of America
| | - Megan Sleep
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL, United States of America
| | - Andrew Lacoste
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL, United States of America
| | - Selene Tan
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL, United States of America
| | - Reid Schuback
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL, United States of America
| | - Lawrence T Reiter
- Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, United States of America; Department of Anatomy & Neurobiology, University of Tennessee Health Science Center, Memphis, TN, United States of America; Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States of America
| | - Atulya Iyengar
- Department of Biological Sciences, University of Alabama, Tuscaloosa, AL, United States of America; Alabama Life Research Institute, University of Alabama, Tuscaloosa, AL, United States of America; Center for Convergent Bioscience and Medicine, University of Alabama, Tuscaloosa, AL, United States of America.
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Cardini A, Melone G, O'Higgins P, Fontaneto D. Exploring motion using geometric morphometrics in microscopic aquatic invertebrates: 'modes' and movement patterns during feeding in a bdelloid rotifer model species. MOVEMENT ECOLOGY 2024; 12:50. [PMID: 39003478 PMCID: PMC11245788 DOI: 10.1186/s40462-024-00491-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 07/08/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND Movement is a defining aspect of animals, but it is rarely studied using quantitative methods in microscopic invertebrates. Bdelloid rotifers are a cosmopolitan class of aquatic invertebrates of great scientific interest because of their ability to survive in very harsh environment and also because they represent a rare example of an ancient lineage that only includes asexually reproducing species. In this class, Adineta ricciae has become a model species as it is unusually easy to culture. Yet, relatively little is known of its ethology and almost nothing on how it behaves during feeding. METHODS To explore feeding behaviour in A. ricciae, as well as to provide an example of application of computational ethology in a microscopic invertebrate, we apply Procrustes motion analysis in combination with ordination and clustering methods to a laboratory bred sample of individuals recorded during feeding. RESULTS We demonstrate that movement during feeding can be accurately described in a simple two-dimensional shape space with three main 'modes' of motion. Foot telescoping, with the body kept straight, is the most frequent 'mode', but it is accompanied by periodic rotations of the foot together with bending while the foot is mostly retracted. CONCLUSIONS Procrustes motion analysis is a relatively simple but effective tool for describing motion during feeding in A. ricciae. The application of this method generates quantitative data that could be analysed in relation to genetic and ecological differences in a variety of experimental settings. The study provides an example that is easy to replicate in other invertebrates, including other microscopic animals whose behavioural ecology is often poorly known.
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Affiliation(s)
- Andrea Cardini
- Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Via Campi 103, 41125, Modena, Italy
- School of Anatomy, Physiology and Human Biology, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia
| | - Giulio Melone
- Università degli Studi di Milano, 20100, Milan, Italy
| | - Paul O'Higgins
- School of Anatomy, Physiology and Human Biology, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia
- Department of Archaeology and Hull York Medical School, University of York, York, YO10 5DD, UK
| | - Diego Fontaneto
- Consiglio Nazionale Delle Ricerche (CNR), Istituto di Ricerca Sulle Acque (IRSA), Corso Tonolli 50, 28922, Verbania Pallanza, Italy.
- National Biodiversity Future Center (NBFC), Piazza Marina 61, 90133, Palermo, Italy.
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Weinreb C, Pearl JE, Lin S, Osman MAM, Zhang L, Annapragada S, Conlin E, Hoffmann R, Makowska S, Gillis WF, Jay M, Ye S, Mathis A, Mathis MW, Pereira T, Linderman SW, Datta SR. Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. Nat Methods 2024; 21:1329-1339. [PMID: 38997595 PMCID: PMC11245396 DOI: 10.1038/s41592-024-02318-2] [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: 04/05/2023] [Accepted: 05/22/2024] [Indexed: 07/14/2024]
Abstract
Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because keypoint data are susceptible to high-frequency jitter that clustering algorithms can mistake for transitions between actions. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ('syllables') from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to identify syllables whose boundaries correspond to natural sub-second discontinuities in pose dynamics. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq also works in multiple species and generalizes beyond the syllable timescale, identifying fast sniff-aligned movements in mice and a spectrum of oscillatory behaviors in fruit flies. Keypoint-MoSeq, therefore, renders accessible the modular structure of behavior through standard video recordings.
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Affiliation(s)
- Caleb Weinreb
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Jonah E Pearl
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Sherry Lin
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Libby Zhang
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | | | - Eli Conlin
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Red Hoffmann
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Sofia Makowska
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Maya Jay
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Shaokai Ye
- Brain Mind and Neuro-X Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Alexander Mathis
- Brain Mind and Neuro-X Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mackenzie W Mathis
- Brain Mind and Neuro-X Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Talmo Pereira
- Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Scott W Linderman
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Department of Statistics, Stanford University, Stanford, CA, USA.
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5
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Lin S, Gillis WF, Weinreb C, Zeine A, Jones SC, Robinson EM, Markowitz J, Datta SR. Characterizing the structure of mouse behavior using Motion Sequencing. Nat Protoc 2024:10.1038/s41596-024-01015-w. [PMID: 38926589 DOI: 10.1038/s41596-024-01015-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 04/12/2024] [Indexed: 06/28/2024]
Abstract
Spontaneous mouse behavior is composed from repeatedly used modules of movement (e.g., rearing, running or grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the effect of drugs, genes, context, sensory stimuli and neural activity on natural behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically inspired method that uses three-dimensional machine vision and unsupervised machine learning to decompose spontaneous mouse behavior into a series of elemental modules called 'syllables'. This protocol is based upon a MoSeq pipeline that includes modules for depth video acquisition, data preprocessing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of the video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other. In addition, we provide instructions for analyzing and visualizing the outputs of keypoint-MoSeq, a recently developed variant of MoSeq that can identify behavioral motifs from keypoints identified from standard (rather than depth) video. This protocol and the accompanying pipeline significantly lower the bar for users without extensive computational ethology experience to adopt this unsupervised, data-driven approach to characterize mouse behavior.
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Affiliation(s)
- Sherry Lin
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Caleb Weinreb
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Ayman Zeine
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Samuel C Jones
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Emma M Robinson
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Jeffrey Markowitz
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
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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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Lv S, Wang J, Chen X, Liao X. STPoseNet: A real-time spatiotemporal network model for robust mouse pose estimation. iScience 2024; 27:109772. [PMID: 38711440 PMCID: PMC11070338 DOI: 10.1016/j.isci.2024.109772] [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: 12/30/2023] [Revised: 03/15/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
Animal behavior analysis plays a crucial role in contemporary neuroscience research. However, the performance of the frame-by-frame approach may degrade in scenarios with occlusions or motion blur. In this study, we propose a spatiotemporal network model based on YOLOv8 to enhance the accuracy of key-point detection in mouse behavioral experimental videos. This model integrates a time-domain tracking strategy comprising two components: the first part utilizes key-point detection results from the previous frame to detect potential target locations in the subsequent frame; the second part employs Kalman filtering to analyze key-point changes prior to detection, allowing for the estimation of missing key-points. In the comparison of pose estimation results between our approach, YOLOv8, DeepLabCut and SLEAP on videos of three mouse behavioral experiments, our approach demonstrated significantly superior performance. This suggests that our method offers a new and effective means of accurately tracking and estimating pose in mice through spatiotemporal processing.
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Affiliation(s)
- Songyan Lv
- Guangxi Key Laboratory of Special Biomedicine & Advanced Institute for Brain and Intelligence, School of Medicine, Guangxi University, Nanning 530004, China
| | - Jincheng Wang
- Guangxi Key Laboratory of Special Biomedicine & Advanced Institute for Brain and Intelligence, School of Medicine, Guangxi University, Nanning 530004, China
| | - Xiaowei Chen
- Guangxi Key Laboratory of Special Biomedicine & Advanced Institute for Brain and Intelligence, School of Medicine, Guangxi University, Nanning 530004, China
| | - Xiang Liao
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing 400030, China
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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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9
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Gattuso H, Nuñez K, de la Rea B, Ermentrout B, Victor J, Nagel K. Inhibitory control of locomotor statistics in walking Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.15.589655. [PMID: 38659800 PMCID: PMC11042290 DOI: 10.1101/2024.04.15.589655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
In order to forage for food, many animals regulate not only specific limb movements but the statistics of locomotor behavior over time, for example switching between long-range dispersal behaviors and more localized search depending on the availability of resources. How pre-motor circuits regulate such locomotor statistics is not clear. Here we took advantage of the robust changes in locomotor statistics evoked by attractive odors in walking Drosophila to investigate their neural control. We began by analyzing the statistics of ground speed and angular velocity during three well-defined motor regimes: baseline walking, upwind running during odor, and search behavior following odor offset. We find that during search behavior, flies adopt higher angular velocities and slower ground speeds, and tend to turn for longer periods of time in one direction. We further find that flies spontaneously adopt periods of different mean ground speed, and that these changes in state influence the length of odor-evoked runs. We next developed a simple physiologically-inspired computational model of locomotor control that can recapitulate these statistical features of fly locomotion. Our model suggests that contralateral inhibition plays a key role both in regulating the difference between baseline and search behavior, and in modulating the response to odor with ground speed. As the fly connectome predicts decussating inhibitory neurons in the lateral accessory lobe (LAL), a pre-motor structure, we generated genetic tools to target these neurons and test their role in behavior. Consistent with our model, we found that activation of neurons labeled in one line increased curvature. In a second line labeling distinct neurons, activation and inactivation strongly and reciprocally regulated ground speed and altered the length of the odor-evoked run. Additional targeted light activation experiments argue that these effects arise from the brain rather than from neurons in the ventral nerve cord, while sparse activation experiments argue that speed control in the second line arises from both LAL neurons and a population of neurons in the dorsal superior medial protocerebrum (SMP). Together, our work develops a biologically plausible computational architecture that captures the statistical features of fly locomotion across behavioral states and identifies potential neural substrates of these computations.
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Affiliation(s)
- Hannah Gattuso
- Department of Neuroscience, NYU School of Medicine, 435 E 30 St. New York, NY 10016, USA
| | - Kavin Nuñez
- Department of Neuroscience, NYU School of Medicine, 435 E 30 St. New York, NY 10016, USA
| | - Beatriz de la Rea
- Department of Neuroscience, NYU School of Medicine, 435 E 30 St. New York, NY 10016, USA
| | - Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jonathan Victor
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Katherine Nagel
- Department of Neuroscience, NYU School of Medicine, 435 E 30 St. New York, NY 10016, USA
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Sleep M, Landaverde S, Lacoste A, Tan S, Schuback R, Reiter LT, Iyengar A. Glial expression of Drosophila UBE3A causes spontaneous seizures modulated by 5-HT signaling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.08.579543. [PMID: 38370819 PMCID: PMC10871353 DOI: 10.1101/2024.02.08.579543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Misexpression of the E3 ubiquitin ligase UBE3A is thought to contribute to a range of neurological disorders. In the context of Dup15q syndrome, excess genomic copies of UBE3A is thought to contribute to the autism, muscle tone and spontaneous seizures characteristic of the disorder. In a Drosophila model of Dup 15q syndrome, it was recently shown glial-driven expression of the UBE3A ortholog dube3a led to a "bang-sensitive" phenotype, where mechanical shock triggers convulsions, suggesting glial dube3a expression contributes to hyperexcitability in flies. Here we directly compare the consequences of glial- and neuronal-driven dube3a expression on motor coordination and neuronal excitability in Drosophila. We utilized IowaFLI tracker and developed a hidden Markov Model to classify seizure-related immobilization. Both glial and neuronal driven dube3a expression led to clear motor phenotypes. However, only glial-driven dube3a expression displayed spontaneous immobilization events, that were exacerbated at high-temperature (38 °C). Using a tethered fly preparation we monitored flight muscle activity, we found glial-driven dube3a flies display spontaneous spike discharges which were bilaterally synchronized indicative of seizure activity. Neither control flies, nor neuronal- dube3a overexpressing flies display such firing patterns. Prior drug screen indicated bang-sensitivity in glial-driven dube3a expressing flies could be suppressed by certain 5-HT modulators. Consistent with this report, we found glial-driven dube3a flies fed the serotonin reuptake inhibitor vortioxetine and the 5HT 2A antagonist ketanserin displayed reduced immobilization and spike bursting. Together these findings highlight the potential for glial pathophysiology to drive Dup15q syndrome-related seizure activity.
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11
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Pang R, Baker C, Murthy M, Pillow J. Inferring neural dynamics of memory during naturalistic social communication. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577404. [PMID: 38328156 PMCID: PMC10849655 DOI: 10.1101/2024.01.26.577404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Memory processes in complex behaviors like social communication require forming representations of the past that grow with time. The neural mechanisms that support such continually growing memory remain unknown. We address this gap in the context of fly courtship, a natural social behavior involving the production and perception of long, complex song sequences. To study female memory for male song history in unrestrained courtship, we present 'Natural Continuation' (NC)-a general, simulation-based model comparison procedure to evaluate candidate neural codes for complex stimuli using naturalistic behavioral data. Applying NC to fly courtship revealed strong evidence for an adaptive population mechanism for how female auditory neural dynamics could convert long song histories into a rich mnemonic format. Song temporal patterning is continually transformed by heterogeneous nonlinear adaptation dynamics, then integrated into persistent activity, enabling common neural mechanisms to retain continuously unfolding information over long periods and yielding state-of-the-art predictions of female courtship behavior. At a population level this coding model produces multi-dimensional advection-diffusion-like responses that separate songs over a continuum of timescales and can be linearly transformed into flexible output signals, illustrating its potential to create a generic, scalable mnemonic format for extended input signals poised to drive complex behavioral responses. This work thus shows how naturalistic behavior can directly inform neural population coding models, revealing here a novel process for memory formation.
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Affiliation(s)
- Rich Pang
- Princeton Neuroscience Institute, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton, NJ and New York, NY, USA
| | - Christa Baker
- Princeton Neuroscience Institute, Princeton, NJ, USA
- Present address: Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton, NJ, USA
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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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13
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Weinreb C, Pearl J, Lin S, Osman MAM, Zhang L, Annapragada S, Conlin E, Hoffman R, Makowska S, Gillis WF, Jay M, Ye S, Mathis A, Mathis MW, Pereira T, Linderman SW, Datta SR. Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.532307. [PMID: 36993589 PMCID: PMC10055085 DOI: 10.1101/2023.03.16.532307] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.
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Affiliation(s)
- Caleb Weinreb
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Jonah Pearl
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Sherry Lin
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Libby Zhang
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | | | - Eli Conlin
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Red Hoffman
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Sofia Makowska
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Maya Jay
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Shaokai Ye
- Brain Mind and Neuro-X Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Alexander Mathis
- Brain Mind and Neuro-X Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mackenzie Weygandt Mathis
- Brain Mind and Neuro-X Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Talmo Pereira
- Salk Institute for Biological Studies, La Jolla, USA
| | - Scott W. Linderman
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Statistics, Stanford University, Stanford, CA, USA
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14
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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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15
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Butler DJ, Keim AP, Ray S, Azim E. Large-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models. Nat Commun 2023; 14:5866. [PMID: 37752123 PMCID: PMC10522643 DOI: 10.1038/s41467-023-41565-3] [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: 05/30/2022] [Accepted: 09/08/2023] [Indexed: 09/28/2023] Open
Abstract
Deep learning-based markerless tracking has revolutionized studies of animal behavior. Yet the generalizability of trained models tends to be limited, as new training data typically needs to be generated manually for each setup or visual environment. With each model trained from scratch, researchers track distinct landmarks and analyze the resulting kinematic data in idiosyncratic ways. Moreover, due to inherent limitations in manual annotation, only a sparse set of landmarks are typically labeled. To address these issues, we developed an approach, which we term GlowTrack, for generating orders of magnitude more training data, enabling models that generalize across experimental contexts. We describe: a) a high-throughput approach for producing hidden labels using fluorescent markers; b) a multi-camera, multi-light setup for simulating diverse visual conditions; and c) a technique for labeling many landmarks in parallel, enabling dense tracking. These advances lay a foundation for standardized behavioral pipelines and more complete scrutiny of movement.
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Affiliation(s)
- Daniel J Butler
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Alexander P Keim
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Shantanu Ray
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Eiman Azim
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA.
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16
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McKenzie-Smith GC, Wolf SW, Ayroles JF, Shaevitz JW. Capturing continuous, long timescale behavioral changes in Drosophila melanogaster postural data. ARXIV 2023:arXiv:2309.04044v1. [PMID: 37731659 PMCID: PMC10508836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Animal behavior spans many timescales, from short, seconds-scale actions to circadian rhythms over many hours to life-long changes during aging. Most quantitative behavior studies have focused on short-timescale behaviors such as locomotion and grooming. Analysis of these data suggests there exists a hierarchy of timescales; however, the limited duration of these experiments prevents the investigation of the full temporal structure. To access longer timescales of behavior, we continuously recorded individual Drosophila melanogaster at 100 frames per second for up to 7 days at a time in featureless arenas on sucrose-agarose media. We use the deep learning framework SLEAP to produce a full-body postural data set for 47 individuals resulting in nearly 2 billion pose instances. We identify stereotyped behaviors such as grooming, proboscis extension, and locomotion and use the resulting ethograms to explore how the flies' behavior varies across time of day and days in the experiment. We find distinct circadian patterns in all of our stereotyped behavior and also see changes in behavior over the course of the experiment as the flies weaken and die.
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Affiliation(s)
| | - Scott W. Wolf
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Julien F. Ayroles
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Joshua W. Shaevitz
- Department of Physics, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
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17
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Krishnamurthy D, Prakash M. Emergent programmable behavior and chaos in dynamically driven active filaments. Proc Natl Acad Sci U S A 2023; 120:e2304981120. [PMID: 37406100 PMCID: PMC10334789 DOI: 10.1073/pnas.2304981120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/16/2023] [Indexed: 07/07/2023] Open
Abstract
How the behavior of cells emerges from their constituent subcellular biochemical and physical parts is an outstanding challenge at the intersection of biology and physics. A remarkable example of single-cell behavior occurs in the ciliate Lacrymaria olor, which hunts for its prey via rapid movements and protrusions of a slender neck, many times the size of the original cell body. The dynamics of this cell neck is powered by a coat of cilia across its length and tip. How a cell can program this active filamentous structure to produce desirable behaviors like search and homing to a target remains unknown. Here, we present an active filament model that allows us to uncover how a "program" (time sequence of active forcing) leads to "behavior" (filament shape dynamics). Our model captures two key features of this system-time-varying activity patterns (extension and compression cycles) and active stresses that are uniquely aligned with the filament geometry-a "follower force" constraint. We show that active filaments under deterministic, time-varying follower forces display rich behaviors including periodic and aperiodic dynamics over long times. We further show that aperiodicity occurs due to a transition to chaos in regions of a biologically accessible parameter space. We also identify a simple nonlinear iterated map of filament shape that approximately predicts long-term behavior suggesting simple, artificial "programs" for filament functions such as homing and searching space. Last, we directly measure the statistical properties of biological programs in L. olor, enabling comparisons between model predictions and experiments.
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Affiliation(s)
| | - Manu Prakash
- Department of Bioengineering, Stanford University, Stanford, CA94305
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18
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Zhu Y, Auer F, Gelnaw H, Davis SN, Hamling KR, May CE, Ahamed H, Ringstad N, Nagel KI, Schoppik D. SAMPL is a high-throughput solution to study unconstrained vertical behavior in small animals. Cell Rep 2023; 42:112573. [PMID: 37267107 PMCID: PMC10592459 DOI: 10.1016/j.celrep.2023.112573] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/27/2023] [Accepted: 05/11/2023] [Indexed: 06/04/2023] Open
Abstract
Balance and movement are impaired in many neurological disorders. Recent advances in behavioral monitoring provide unprecedented access to posture and locomotor kinematics but without the throughput and scalability necessary to screen candidate genes/potential therapeutics. Here, we present a scalable apparatus to measure posture and locomotion (SAMPL). SAMPL includes extensible hardware and open-source software with real-time processing and can acquire data from D. melanogaster, C. elegans, and D. rerio as they move vertically. Using SAMPL, we define how zebrafish balance as they navigate vertically and discover small but systematic variations among kinematic parameters between genetic backgrounds. We demonstrate SAMPL's ability to resolve differences in posture and navigation as a function of effect size and data gathered, providing key data for screens. SAMPL is therefore both a tool to model balance and locomotor disorders and an exemplar of how to scale apparatus to support screens.
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Affiliation(s)
- Yunlu Zhu
- Department of Otolaryngology, New York University Grossman School of Medicine, New York, NY 10016, USA; The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Franziska Auer
- Department of Otolaryngology, New York University Grossman School of Medicine, New York, NY 10016, USA; The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Hannah Gelnaw
- Department of Otolaryngology, New York University Grossman School of Medicine, New York, NY 10016, USA; The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Samantha N Davis
- Department of Otolaryngology, New York University Grossman School of Medicine, New York, NY 10016, USA; The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Kyla R Hamling
- Department of Otolaryngology, New York University Grossman School of Medicine, New York, NY 10016, USA; The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Christina E May
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Hassan Ahamed
- Department of Cell Biology, Skirball Institute of Biomolecular Medicine, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Niels Ringstad
- Department of Cell Biology, Skirball Institute of Biomolecular Medicine, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Katherine I Nagel
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - David Schoppik
- Department of Otolaryngology, New York University Grossman School of Medicine, New York, NY 10016, USA; The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA.
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19
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Voloh B, Eisenreich BR, Maisson DJN, Ebitz RB, Park HS, Hayden BY, Zimmermann J. Hierarchical organization of rhesus macaque behavior. OXFORD OPEN NEUROSCIENCE 2023; 2:kvad006. [PMID: 37577290 PMCID: PMC10421634 DOI: 10.1093/oons/kvad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/11/2023] [Accepted: 06/12/2023] [Indexed: 08/15/2023]
Abstract
Primatologists, psychologists and neuroscientists have long hypothesized that primate behavior is highly structured. However, delineating that structure has been impossible due to the difficulties of precision behavioral tracking. Here we analyzed a dataset consisting of continuous measures of the 3D position of two male rhesus macaques (Macaca mulatta) performing three different tasks in a large unrestrained environment over several hours. Using an unsupervised embedding approach on the tracked joints, we identified commonly repeated pose patterns, which we call postures. We found that macaques' behavior is characterized by 49 distinct postures, lasting an average of 0.6 seconds. We found evidence that behavior is hierarchically organized, in that transitions between poses tend to occur within larger modules, which correspond to identifiable actions; these actions are further organized hierarchically. Our behavioral decomposition allows us to identify universal (cross-individual and cross-task) and unique (specific to each individual and task) principles of behavior. These results demonstrate the hierarchical nature of primate behavior, provide a method for the automated ethogramming of primate behavior, and provide important constraints on neural models of pose generation.
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Affiliation(s)
- Benjamin Voloh
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
| | - Benjamin R Eisenreich
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
| | - David J-N Maisson
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
| | - R Becket Ebitz
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
| | - Hyun Soo Park
- Department of Computer Science and Engineering, University of Minnesota, 40 Church St, Minneapolis, MN 55455, USA
| | - Benjamin Y Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
| | - Jan Zimmermann
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, 1 Baylor Plaza, Houston, TX 77030
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20
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Ravens A, Stacher-Hörndli CN, Emery J, Steinwand S, Shepherd JD, Gregg C. Arc regulates a second-guessing cognitive bias during naturalistic foraging through effects on discrete behavior modules. iScience 2023; 26:106761. [PMID: 37216088 PMCID: PMC10196573 DOI: 10.1016/j.isci.2023.106761] [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: 08/04/2022] [Revised: 11/29/2022] [Accepted: 04/24/2023] [Indexed: 05/24/2023] Open
Abstract
Foraging in animals relies on innate decision-making heuristics that can result in suboptimal cognitive biases in some contexts. The mechanisms underlying these biases are not well understood, but likely involve strong genetic effects. To explore this, we studied fasted mice using a naturalistic foraging paradigm and discovered an innate cognitive bias called "second-guessing." This involves repeatedly investigating an empty former food patch instead of consuming available food, which hinders the mice from maximizing feeding benefits. The synaptic plasticity gene Arc is revealed to play a role in this bias, as Arc-deficient mice did not exhibit second-guessing and consumed more food. In addition, unsupervised machine learning decompositions of foraging identified specific behavior sequences, or "modules", that are affected by Arc. These findings highlight the genetic basis of cognitive biases in decision making, show links between behavior modules and cognitive bias, and provide insight into the ethological roles of Arc in naturalistic foraging.
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Affiliation(s)
- Alicia Ravens
- University of Utah, Department of Neurobiology, Salt Lake City, UT, USA
| | | | - Jared Emery
- Storyline Health Inc., Salt Lake City, UT, USA
| | - Susan Steinwand
- University of Utah, Department of Neurobiology, Salt Lake City, UT, USA
| | - Jason D. Shepherd
- University of Utah, Department of Neurobiology, Salt Lake City, UT, USA
- University of Utah, Department of Biochemistry School of Medicine, Salt Lake City, UT, USA
- University of Utah, Department of Ophthalmology & Visual Sciences, Salt Lake City, UT, USA
| | - Christopher Gregg
- University of Utah, Department of Neurobiology, Salt Lake City, UT, USA
- University of Utah, Department of Human Genetics, Salt Lake City, UT, USA
- Storyline Health Inc., Salt Lake City, UT, USA
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21
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Zeraati R, Shi YL, Steinmetz NA, Gieselmann MA, Thiele A, Moore T, Levina A, Engel TA. Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity. Nat Commun 2023; 14:1858. [PMID: 37012299 PMCID: PMC10070246 DOI: 10.1038/s41467-023-37613-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 03/24/2023] [Indexed: 04/05/2023] Open
Abstract
Intrinsic timescales characterize dynamics of endogenous fluctuations in neural activity. Variation of intrinsic timescales across the neocortex reflects functional specialization of cortical areas, but less is known about how intrinsic timescales change during cognitive tasks. We measured intrinsic timescales of local spiking activity within columns of area V4 in male monkeys performing spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales, fast and slow. The slow timescale increased when monkeys attended to the receptive fields location and correlated with reaction times. By evaluating predictions of several network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and attentional modulation of timescales results from an increase in the efficacy of recurrent interactions. Our results suggest that multiple timescales may arise from the spatial connectivity in the visual cortex and flexibly change with the cognitive state due to dynamic effective interactions between neurons.
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Affiliation(s)
- Roxana Zeraati
- International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, University of Tübingen, Tübingen, Germany
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Yan-Liang Shi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Marc A Gieselmann
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Alexander Thiele
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Tirin Moore
- Department of Neurobiology and Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Anna Levina
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
- Department of Computer Science, University of Tübingen, Tübingen, Germany.
- Bernstein Center for Computational Neuroscience Tübingen, Tübingen, Germany.
| | - Tatiana A Engel
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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22
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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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23
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Zhu Y, Auer F, Gelnaw H, Davis SN, Hamling KR, May CE, Ahamed H, Ringstad N, Nagel KI, Schoppik D. Scalable Apparatus to Measure Posture and Locomotion (SAMPL): a high-throughput solution to study unconstrained vertical behavior in small animals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.07.523102. [PMID: 36712122 PMCID: PMC9881893 DOI: 10.1101/2023.01.07.523102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Balance and movement are impaired in a wide variety of neurological disorders. Recent advances in behavioral monitoring provide unprecedented access to posture and locomotor kinematics, but without the throughput and scalability necessary to screen candidate genes / potential therapeutics. We present a powerful solution: a Scalable Apparatus to Measure Posture and Locomotion (SAMPL). SAMPL includes extensible imaging hardware and low-cost open-source acquisition software with real-time processing. We first demonstrate that SAMPL's hardware and acquisition software can acquire data from from D. melanogaster, C. elegans, and D. rerio as they move vertically. Next, we leverage SAMPL's throughput to rapidly (two weeks) gather a new zebrafish dataset. We use SAMPL's analysis and visualization tools to replicate and extend our current understanding of how zebrafish balance as they navigate through a vertical environment. Next, we discover (1) that key kinematic parameters vary systematically with genetic background, and (2) that such background variation is small relative to the changes that accompany early development. Finally, we simulate SAMPL's ability to resolve differences in posture or vertical navigation as a function of affect size and data gathered -- key data for screens. Taken together, our apparatus, data, and analysis provide a powerful solution for labs using small animals to investigate balance and locomotor disorders at scale. More broadly, SAMPL is both an adaptable resource for labs looking process videographic measures of behavior in real-time, and an exemplar of how to scale hardware to enable the throughput necessary for screening.
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Affiliation(s)
- Yunlu Zhu
- Department. of Otolaryngology, New York University Grossman School of Medicine
- The Neuroscience Institute, New York University Grossman School of Medicine
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine
| | - Franziska Auer
- Department. of Otolaryngology, New York University Grossman School of Medicine
- The Neuroscience Institute, New York University Grossman School of Medicine
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine
| | - Hannah Gelnaw
- Department. of Otolaryngology, New York University Grossman School of Medicine
- The Neuroscience Institute, New York University Grossman School of Medicine
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine
| | - Samantha N. Davis
- Department. of Otolaryngology, New York University Grossman School of Medicine
- The Neuroscience Institute, New York University Grossman School of Medicine
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine
| | - Kyla R. Hamling
- Department. of Otolaryngology, New York University Grossman School of Medicine
- The Neuroscience Institute, New York University Grossman School of Medicine
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine
| | - Christina E. May
- The Neuroscience Institute, New York University Grossman School of Medicine
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine
| | - Hassan Ahamed
- Department of Cell Biology, Skirball Institute of Biomolecular Medicine, New York University Grossman School of Medicine
| | - Niels Ringstad
- Department of Cell Biology, Skirball Institute of Biomolecular Medicine, New York University Grossman School of Medicine
| | - Katherine I. Nagel
- The Neuroscience Institute, New York University Grossman School of Medicine
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine
| | - David Schoppik
- Department. of Otolaryngology, New York University Grossman School of Medicine
- The Neuroscience Institute, New York University Grossman School of Medicine
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine
- Lead Contact
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24
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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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Geng Y, Yates C, Peterson RT. Social behavioral profiling by unsupervised deep learning reveals a stimulative effect of dopamine D3 agonists on zebrafish sociality. CELL REPORTS METHODS 2023; 3:100381. [PMID: 36814839 PMCID: PMC9939379 DOI: 10.1016/j.crmeth.2022.100381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 11/15/2022] [Accepted: 12/12/2022] [Indexed: 01/07/2023]
Abstract
It has been a major challenge to systematically evaluate and compare how pharmacological perturbations influence social behavioral outcomes. Although some pharmacological agents are known to alter social behavior, precise description and quantification of such effects have proven difficult. We developed a scalable social behavioral assay for zebrafish named ZeChat based on unsupervised deep learning to characterize sociality at high resolution. High-dimensional and dynamic social behavioral phenotypes are automatically classified using this method. By screening a neuroactive compound library, we found that different classes of chemicals evoke distinct patterns of social behavioral fingerprints. By examining these patterns, we discovered that dopamine D3 agonists possess a social stimulative effect on zebrafish. The D3 agonists pramipexole, piribedil, and 7-hydroxy-DPAT-HBr rescued social deficits in a valproic-acid-induced zebrafish autism model. The ZeChat platform provides a promising approach for dissecting the pharmacology of social behavior and discovering novel social-modulatory compounds.
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Affiliation(s)
- Yijie Geng
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher Yates
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
| | - Randall T. Peterson
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
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26
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Singh SH, van Breugel F, Rao RPN, Brunton BW. Emergent behaviour and neural dynamics in artificial agents tracking odour plumes. NAT MACH INTELL 2023; 5:58-70. [PMID: 37886259 PMCID: PMC10601839 DOI: 10.1038/s42256-022-00599-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 12/01/2022] [Indexed: 01/26/2023]
Abstract
Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have been studied experimentally. Here we take a complementary in silico approach to develop an integrated understanding of their behaviour and neural computations. Specifically, we train artificial recurrent neural network agents using deep reinforcement learning to locate the source of simulated odour plumes that mimic features of plumes in a turbulent flow. Interestingly, the agents' emergent behaviours resemble those of flying insects, and the recurrent neural networks learn to compute task-relevant variables with distinct dynamic structures in population activity. Our analyses put forward a testable behavioural hypothesis for tracking plumes in changing wind direction, and we provide key intuitions for memory requirements and neural dynamics in odour plume tracking.
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27
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Casarrubea M, Leca JB, Gunst N, Jonsson GK, Portell M, Di Giovanni G, Aiello S, Crescimanno G. Structural analyses in the study of behavior: From rodents to non-human primates. Front Psychol 2022; 13:1033561. [PMID: 36467208 PMCID: PMC9716108 DOI: 10.3389/fpsyg.2022.1033561] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/25/2022] [Indexed: 09/03/2023] Open
Abstract
The term "structure" indicates a set of components that, in relation to each other, shape an organic complex. Such a complex takes on essential connotations of functionally unitary entity resulting from the mutual relationships of its constituent elements. In a broader sense, we can use the word "structure" to define the set of relationships among the elements of an emergent system that is not determined by the mere algebraic sum of these elements, but by the interdependence relationships of these components from which the function of the entire structure itself derives. The behavior of an integrated living being can be described in structural terms via an ethogram, defined as an itemized list of behavioral units. Akin to an architectural structure, a behavioral structure arises from the reciprocal relationships that the individual units of behavior establish. Like an architectural structure, the function of the resulting behaving complex emerges from the relationships of the parts. Hence, studying behavior in its wholeness necessitates not only the identification of its constitutive units in their autarchic individuality, but also, and importantly, some understanding of their relationships. This paper aimed to critically review different methods to study behavior in structural terms. First, we emphasized the utilization of T-pattern analysis, i.e., one of the most effective and reliable tools to provide structural information on behavior. Second, we discussed the application of other methodological approaches that are based on the analysis of transition matrices, such as hierarchical clustering, stochastic analyses, and adjusted residuals. Unlike T-pattern analysis, these methods allow researchers to explore behavioral structure beyond its temporal characteristics and through other relational constraints. After an overview of how these methods are used in the study of animal behavior, from rodents to non-human primates, we discussed the specificities, advantages and challenges of each approach. This paper could represent a useful background for all scientists who intend to study behavior both quantitatively and structurally, that is in terms of the reciprocal relationships that the various units of a given behavioral repertoire normally weave together.
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Affiliation(s)
- Maurizio Casarrubea
- Laboratory of Behavioral Physiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), Human Physiology Section “Giuseppe Pagano”, University of Palermo, Palermo, Italy
| | - Jean-Baptiste Leca
- Department of Psychology, University of Lethbridge, Lethbridge, AB, Canada
- School of Natural and Engineering Sciences, National Institute of Advanced Studies, Bangalore, India
| | - Noëlle Gunst
- Department of Psychology, University of Lethbridge, Lethbridge, AB, Canada
| | - Gudberg K. Jonsson
- Human Behavior Laboratory, School of Health Sciences, University of Iceland, Reykjavík, Iceland
| | - Mariona Portell
- Department of Psychobiology and Methodology of Health Sciences, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | - Giuseppe Di Giovanni
- Laboratory of Neurophysiology, Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
| | - Stefania Aiello
- Laboratory of Behavioral Physiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), Human Physiology Section “Giuseppe Pagano”, University of Palermo, Palermo, Italy
| | - Giuseppe Crescimanno
- Laboratory of Behavioral Physiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), Human Physiology Section “Giuseppe Pagano”, University of Palermo, Palermo, Italy
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28
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Marzen SE, Crutchfield JP. Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1675. [PMID: 36421529 PMCID: PMC9689584 DOI: 10.3390/e24111675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/08/2022] [Accepted: 11/09/2022] [Indexed: 06/16/2023]
Abstract
Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network's universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.
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Affiliation(s)
- Sarah E. Marzen
- W. M. Keck Science Department of Pitzer, Scripps, and Claremont McKenna College, Claremont, CA 91711, USA
| | - James P. Crutchfield
- Complexity Sciences Center and Physics and Astronomy Department, University of California at Davis, One Shields Avenue, Davis, CA 95616, USA
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29
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Larson BT, Garbus J, Pollack JB, Marshall WF. A unicellular walker controlled by a microtubule-based finite-state machine. Curr Biol 2022; 32:3745-3757.e7. [PMID: 35963241 PMCID: PMC9474717 DOI: 10.1016/j.cub.2022.07.034] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/20/2022] [Accepted: 07/14/2022] [Indexed: 11/21/2022]
Abstract
Cells are complex biochemical systems whose behaviors emerge from interactions among myriad molecular components. Computation is often invoked as a general framework for navigating this cellular complexity. However, it is unclear how cells might embody computational processes such that the theories of computation, including finite-state machine models, could be productively applied. Here, we demonstrate finite-state-machine-like processing embodied in cells using the walking behavior of Euplotes eurystomus, a ciliate that walks across surfaces using fourteen motile appendages (cirri). We found that cellular walking entails regulated transitions among a discrete set of gait states. The set of observed transitions decomposes into a small group of high-probability, temporally irreversible transitions and a large group of low-probability, time-symmetric transitions, thus revealing stereotypy in the sequential patterns of state transitions. Simulations and experiments suggest that the sequential logic of the gait is functionally important. Taken together, these findings implicate a finite-state-machine-like process. Cirri are connected by microtubule bundles (fibers), and we found that the dynamics of cirri involved in different state transitions are associated with the structure of the fiber system. Perturbative experiments revealed that the fibers mediate gait coordination, suggesting a mechanical basis of gait control.
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Affiliation(s)
- Ben T Larson
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Jack Garbus
- Computer Science Department, Brandeis University, Waltham, MA 02453, USA
| | - Jordan B Pollack
- Computer Science Department, Brandeis University, Waltham, MA 02453, USA
| | - Wallace F Marshall
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA.
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30
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Flavell SW, Gogolla N, Lovett-Barron M, Zelikowsky M. The emergence and influence of internal states. Neuron 2022; 110:2545-2570. [PMID: 35643077 PMCID: PMC9391310 DOI: 10.1016/j.neuron.2022.04.030] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 02/11/2022] [Accepted: 04/27/2022] [Indexed: 01/09/2023]
Abstract
Animal behavior is shaped by a variety of "internal states"-partially hidden variables that profoundly shape perception, cognition, and action. The neural basis of internal states, such as fear, arousal, hunger, motivation, aggression, and many others, is a prominent focus of research efforts across animal phyla. Internal states can be inferred from changes in behavior, physiology, and neural dynamics and are characterized by properties such as pleiotropy, persistence, scalability, generalizability, and valence. To date, it remains unclear how internal states and their properties are generated by nervous systems. Here, we review recent progress, which has been driven by advances in behavioral quantification, cellular manipulations, and neural population recordings. We synthesize research implicating defined subsets of state-inducing cell types, widespread changes in neural activity, and neuromodulation in the formation and updating of internal states. In addition to highlighting the significance of these findings, our review advocates for new approaches to clarify the underpinnings of internal brain states across the animal kingdom.
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Affiliation(s)
- Steven W Flavell
- Picower Institute for Learning and Memory, Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Nadine Gogolla
- Emotion Research Department, Max Planck Institute of Psychiatry, 80804 Munich, Germany; Circuits for Emotion Research Group, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany.
| | - Matthew Lovett-Barron
- Division of Biological Sciences-Neurobiology Section, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Moriel Zelikowsky
- Department of Neurobiology, University of Utah, Salt Lake City, UT 84112, USA.
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31
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Nilsson DE, Smolka J, Bok M. The vertical light-gradient and its potential impact on animal distribution and behavior. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.951328] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
The visual environment provides vital cues allowing animals to assess habitat quality, weather conditions or measure time of day. Together with other sensory cues and physiological conditions, the visual environment sets behavioral states that make the animal more prone to engage in some behaviors, and less in others. This master-control of behavior serves a fundamental and essential role in determining the distribution and behavior of all animals. Although it is obvious that visual information contains vital input for setting behavioral states, the precise nature of these visual cues remains unknown. Here we use a recently described method to quantify the distribution of light reaching animals’ eyes in different environments. The method records the vertical gradient (as a function of elevation angle) of intensity, spatial structure and spectral balance. Comparison of measurements from different types of environments, weather conditions, times of day, and seasons reveal that these aspects can be readily discriminated from one another. The vertical gradients of radiance, spatial structure (contrast) and color are thus reliable indicators that are likely to have a strong impact on animal behavior and spatial distribution.
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32
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Mazzucato L. Neural mechanisms underlying the temporal organization of naturalistic animal behavior. eLife 2022; 11:e76577. [PMID: 35792884 PMCID: PMC9259028 DOI: 10.7554/elife.76577] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/07/2022] [Indexed: 12/17/2022] Open
Abstract
Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.
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Affiliation(s)
- Luca Mazzucato
- Institute of Neuroscience, Departments of Biology, Mathematics and Physics, University of OregonEugeneUnited States
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33
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Cavanagh H, Kempe D, Mazalo JK, Biro M, Endres RG. T cell morphodynamics reveal periodic shape oscillations in three-dimensional migration. J R Soc Interface 2022; 19:20220081. [PMID: 35537475 PMCID: PMC9090490 DOI: 10.1098/rsif.2022.0081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
T cells use sophisticated shape dynamics (morphodynamics) to migrate towards and neutralize infected and cancerous cells. However, there is limited quantitative understanding of the migration process in three-dimensional extracellular matrices (ECMs) and across timescales. Here, we leveraged recent advances in lattice light-sheet microscopy to quantitatively explore the three-dimensional morphodynamics of migrating T cells at high spatio-temporal resolution. We first developed a new shape descriptor based on spherical harmonics, incorporating key polarization information of the uropod. We found that the shape space of T cells is low-dimensional. At the behavioural level, run-and-stop migration modes emerge at approximately 150 s, and we mapped the morphodynamic composition of each mode using multiscale wavelet analysis, finding 'stereotyped' motifs. Focusing on the run mode, we found morphodynamics oscillating periodically (every approx. 100 s) that can be broken down into a biphasic process: front-widening with retraction of the uropod, followed by a rearward surface motion and forward extension, where intercalation with the ECM in both of these steps likely facilitates forward motion. Further application of these methods may enable the comparison of T cell migration across different conditions (e.g. differentiation, activation, tissues and drug treatments) and improve the precision of immunotherapeutic development.
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Affiliation(s)
- Henry Cavanagh
- Imperial College London, Centre for Integrative Systems Biology and Bioinformatics, London SW7 2BU, UK
| | - Daryan Kempe
- EMBL Australia, Single Molecule Science Node, School of Medical Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Jessica K Mazalo
- EMBL Australia, Single Molecule Science Node, School of Medical Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Maté Biro
- EMBL Australia, Single Molecule Science Node, School of Medical Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Robert G Endres
- Imperial College London, Centre for Integrative Systems Biology and Bioinformatics, London SW7 2BU, UK
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34
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Klibaite U, Kislin M, Verpeut JL, Bergeler S, Sun X, Shaevitz JW, Wang SSH. Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models. Mol Autism 2022; 13:12. [PMID: 35279205 PMCID: PMC8917660 DOI: 10.1186/s13229-022-00492-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 02/25/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD. METHODS We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. RESULTS Both Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. LIMITATIONS Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. CONCLUSIONS Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.
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Affiliation(s)
- Ugne Klibaite
- Department of Organismic and Evolutionary Biology, Harvard University, 52 Oxford St, 02138, Cambridge, MA, USA.
| | - Mikhail Kislin
- Princeton Neuroscience Institute, Princeton University, Washington Rd, 08544, Princeton, NJ, USA
| | - Jessica L Verpeut
- Princeton Neuroscience Institute, Princeton University, Washington Rd, 08544, Princeton, NJ, USA
| | - Silke Bergeler
- Department of Physics, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Washington Rd, 08544, Princeton, NJ, USA
| | - Xiaoting Sun
- Princeton Neuroscience Institute, Princeton University, Washington Rd, 08544, Princeton, NJ, USA
| | - Joshua W Shaevitz
- Department of Physics, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Washington Rd, 08544, Princeton, NJ, USA.
| | - Samuel S-H Wang
- Princeton Neuroscience Institute, Princeton University, Washington Rd, 08544, Princeton, NJ, USA.
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35
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Sainburg T, Mai A, Gentner TQ. Long-range sequential dependencies precede complex syntactic production in language acquisition. Proc Biol Sci 2022; 289:20212657. [PMID: 35259983 PMCID: PMC8905171 DOI: 10.1098/rspb.2021.2657] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/01/2022] [Indexed: 12/27/2022] Open
Abstract
To convey meaning, human language relies on hierarchically organized, long-range relationships spanning words, phrases, sentences and discourse. As the distances between elements (e.g. phonemes, characters, words) in human language sequences increase, the strength of the long-range relationships between those elements decays following a power law. This power-law relationship has been attributed variously to long-range sequential organization present in human language syntax, semantics and discourse structure. However, non-linguistic behaviours in numerous phylogenetically distant species, ranging from humpback whale song to fruit fly motility, also demonstrate similar long-range statistical dependencies. Therefore, we hypothesized that long-range statistical dependencies in human speech may occur independently of linguistic structure. To test this hypothesis, we measured long-range dependencies in several speech corpora from children (aged 6 months-12 years). We find that adult-like power-law statistical dependencies are present in human vocalizations at the earliest detectable ages, prior to the production of complex linguistic structure. These linguistic structures cannot, therefore, be the sole cause of long-range statistical dependencies in language.
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Affiliation(s)
- Tim Sainburg
- Department of Psychology, Kavli Institute for Brain and Mind, UC San Diego, La Jolla, CA 92093, USA
- Center for Academic Research & Training in Anthropogeny, Kavli Institute for Brain and Mind, UC San Diego, La Jolla, CA 92093, USA
| | - Anna Mai
- Department of Linguistics, Kavli Institute for Brain and Mind, UC San Diego, La Jolla, CA 92093, USA
| | - Timothy Q. Gentner
- Department of Psychology, Kavli Institute for Brain and Mind, UC San Diego, La Jolla, CA 92093, USA
- Neurosciences Graduate Program, Neurobiology Section, Kavli Institute for Brain and Mind, UC San Diego, La Jolla, CA 92093, USA
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36
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Kline AG, Palmer SE. Gaussian Information Bottleneck and the Non-Perturbative Renormalization Group. NEW JOURNAL OF PHYSICS 2022; 24:033007. [PMID: 35368649 PMCID: PMC8967309 DOI: 10.1088/1367-2630/ac395d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The renormalization group (RG) is a class of theoretical techniques used to explain the collective physics of interacting, many-body systems. It has been suggested that the RG formalism may be useful in finding and interpreting emergent low-dimensional structure in complex systems outside of the traditional physics context, such as in biology or computer science. In such contexts, one common dimensionality-reduction framework already in use is information bottleneck (IB), in which the goal is to compress an "input" signal X while maximizing its mutual information with some stochastic "relevance" variable Y. IB has been applied in the vertebrate and invertebrate processing systems to characterize optimal encoding of the future motion of the external world. Other recent work has shown that the RG scheme for the dimer model could be "discovered" by a neural network attempting to solve an IB-like problem. This manuscript explores whether IB and any existing formulation of RG are formally equivalent. A class of soft-cutoff non-perturbative RG techniques are defined by families of non-deterministic coarsening maps, and hence can be formally mapped onto IB, and vice versa. For concreteness, this discussion is limited entirely to Gaussian statistics (GIB), for which IB has exact, closed-form solutions. Under this constraint, GIB has a semigroup structure, in which successive transformations remain IB-optimal. Further, the RG cutoff scheme associated with GIB can be identified. Our results suggest that IB can be used to impose a notion of "large scale" structure, such as biological function, on an RG procedure.
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Affiliation(s)
- Adam G Kline
- Department of Physics, The University of Chicago, Chicago IL 60637
| | - Stephanie E Palmer
- Department of Organismal Biology and Anatomy and Department of Physics, The University of Chicago, Chicago IL 60637
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37
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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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38
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Pezzulo G, Parr T, Friston K. The evolution of brain architectures for predictive coding and active inference. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200531. [PMID: 34957844 PMCID: PMC8710884 DOI: 10.1098/rstb.2020.0531] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/08/2021] [Indexed: 01/13/2023] Open
Abstract
This article considers the evolution of brain architectures for predictive processing. We argue that brain mechanisms for predictive perception and action are not late evolutionary additions of advanced creatures like us. Rather, they emerged gradually from simpler predictive loops (e.g. autonomic and motor reflexes) that were a legacy from our earlier evolutionary ancestors-and were key to solving their fundamental problems of adaptive regulation. We characterize simpler-to-more-complex brains formally, in terms of generative models that include predictive loops of increasing hierarchical breadth and depth. These may start from a simple homeostatic motif and be elaborated during evolution in four main ways: these include the multimodal expansion of predictive control into an allostatic loop; its duplication to form multiple sensorimotor loops that expand an animal's behavioural repertoire; and the gradual endowment of generative models with hierarchical depth (to deal with aspects of the world that unfold at different spatial scales) and temporal depth (to select plans in a future-oriented manner). In turn, these elaborations underwrite the solution to biological regulation problems faced by increasingly sophisticated animals. Our proposal aligns neuroscientific theorising-about predictive processing-with evolutionary and comparative data on brain architectures in different animal species. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Via S. Martino della Battaglia, 44, 00185 Rome, Italy
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK
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39
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Nilsson DE. The Evolution of Visual Roles – Ancient Vision Versus Object Vision. Front Neuroanat 2022; 16:789375. [PMID: 35221931 PMCID: PMC8863595 DOI: 10.3389/fnana.2022.789375] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/20/2022] [Indexed: 12/05/2022] Open
Abstract
Just like other complex biological features, image vision (multi-pixel light sensing) did not evolve suddenly. Animal visual systems have a long prehistory of non-imaging light sensitivity. The first spatial vision was likely very crude with only few pixels, and evolved to improve orientation behaviors previously supported by single-channel directional photoreception. The origin of image vision was simply a switch from single to multiple spatial channels, which improved the behaviors for finding a suitable habitat and position itself within it. Orientation based on spatial vision obviously involves active guidance of behaviors but, by necessity, also assessment of habitat suitability and environmental conditions. These conditions are crucial for deciding when to forage, reproduce, seek shelter, rest, etc. When spatial resolution became good enough to see other animals and interact with them, a whole range of new visual roles emerged: pursuit, escape, communication and other interactions. All these new visual roles require entirely new types of visual processing. Objects needed to be separated from the background, identified and classified to make the correct choice of interaction. Object detection and identification can be used actively to guide behaviors but of course also to assess the over-all situation. Visual roles can thus be classified as either ancient non-object-based tasks, or object vision. Each of these two categories can also be further divided into active visual tasks and visual assessment tasks. This generates four major categories of vision into which I propose that all visual roles can be categorized.
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40
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Ebbesen CL, Froemke RC. Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography. Nat Commun 2022; 13:593. [PMID: 35105858 PMCID: PMC8807631 DOI: 10.1038/s41467-022-28153-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 01/06/2022] [Indexed: 12/25/2022] Open
Abstract
Social interactions powerfully impact the brain and the body, but high-resolution descriptions of these important physical interactions and their neural correlates are lacking. Currently, most studies rely on labor-intensive methods such as manual annotation. Scalable and objective tracking methods are required to understand the neural circuits underlying social behavior. Here we describe a hardware/software system and analysis pipeline that combines 3D videography, deep learning, physical modeling, and GPU-accelerated robust optimization, with automatic analysis of neuronal receptive fields recorded in interacting mice. Our system ("3DDD Social Mouse Tracker") is capable of fully automatic multi-animal tracking with minimal errors (including in complete darkness) during complex, spontaneous social encounters, together with simultaneous electrophysiological recordings. We capture posture dynamics of multiple unmarked mice with high spatiotemporal precision (~2 mm, 60 frames/s). A statistical model that relates 3D behavior and neural activity reveals multiplexed 'social receptive fields' of neurons in barrel cortex. Our approach could be broadly useful for neurobehavioral studies of multiple animals interacting in complex low-light environments.
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Affiliation(s)
- Christian L Ebbesen
- Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, NY, 10016, USA.
- Neuroscience Institute, New York University School of Medicine, New York, NY, 10016, USA.
- Department of Otolaryngology, New York University School of Medicine, New York, NY, 10016, USA.
- Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, 10016, USA.
- Center for Neural Science, New York University, New York, NY, 10003, USA.
| | - Robert C Froemke
- Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, NY, 10016, USA.
- Neuroscience Institute, New York University School of Medicine, New York, NY, 10016, USA.
- Department of Otolaryngology, New York University School of Medicine, New York, NY, 10016, USA.
- Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, 10016, USA.
- Center for Neural Science, New York University, New York, NY, 10003, USA.
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41
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Fleig P, Kramar M, Wilczek M, Alim K. Emergence of behaviour in a self-organized living matter network. eLife 2022; 11:62863. [PMID: 35060901 PMCID: PMC8782570 DOI: 10.7554/elife.62863] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/20/2021] [Indexed: 12/16/2022] Open
Abstract
What is the origin of behaviour? Although typically associated with a nervous system, simple organisms also show complex behaviours. Among them, the slime mold Physarum polycephalum, a giant single cell, is ideally suited to study emergence of behaviour. Here, we show how locomotion and morphological adaptation behaviour emerge from self-organized patterns of rhythmic contractions of the actomyosin lining of the tubes making up the network-shaped organism. We quantify the spatio-temporal contraction dynamics by decomposing experimentally recorded contraction patterns into spatial contraction modes. Notably, we find a continuous spectrum of modes, as opposed to a few dominant modes. Our data suggests that the continuous spectrum of modes allows for dynamic transitions between a plethora of specific behaviours with transitions marked by highly irregular contraction states. By mapping specific behaviours to states of active contractions, we provide the basis to understand behaviour’s complexity as a function of biomechanical dynamics.
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Affiliation(s)
- Philipp Fleig
- Department of Physics & Astronomy, University of Pennsylvania
- Max Planck Institute for Dynamics and Self-Organization
| | - Mirna Kramar
- Max Planck Institute for Dynamics and Self-Organization
| | | | - Karen Alim
- Max Planck Institute for Dynamics and Self-Organization
- Physik-Department and Center for Protein Assemblies, Technische Universität München
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42
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Metastable attractors explain the variable timing of stable behavioral action sequences. Neuron 2022; 110:139-153.e9. [PMID: 34717794 PMCID: PMC9194601 DOI: 10.1016/j.neuron.2021.10.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/30/2021] [Accepted: 10/05/2021] [Indexed: 01/07/2023]
Abstract
The timing of self-initiated actions shows large variability even when they are executed in stable, well-learned sequences. Could this mix of reliability and stochasticity arise within the same neural circuit? We trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), which is known to reflect trial-by-trial action-timing fluctuations. Using hidden Markov models, we established a dictionary between activity patterns and actions. We then showed that metastable attractors, representing activity patterns with a reliable sequential structure and large transition timing variability, could be produced by reciprocally coupling a high-dimensional recurrent network and a low-dimensional feedforward one. Transitions between attractors relied on correlated variability in this mesoscale feedback loop, predicting a specific structure of low-dimensional correlations that were empirically verified in M2 recordings. Our results suggest a novel mesoscale network motif based on correlated variability supporting naturalistic animal behavior.
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43
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Reddy G, Desban L, Tanaka H, Roussel J, Mirat O, Wyart C. A lexical approach for identifying behavioural action sequences. PLoS Comput Biol 2022; 18:e1009672. [PMID: 35007275 PMCID: PMC8782473 DOI: 10.1371/journal.pcbi.1009672] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 01/21/2022] [Accepted: 11/16/2021] [Indexed: 12/14/2022] Open
Abstract
Animals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural response to a distributed stimulus in unrestrained animals. Existing models seek to describe the dynamics of behaviour or segment individual locomotor episodes rather than to identify the rare and transient sequences of locomotor episodes that make up the behavioural response. To fill this gap, we develop a lexical, hierarchical model of behaviour. We designed an unsupervised algorithm called "BASS" to efficiently identify and segment recurring behavioural action sequences transiently occurring in long behavioural recordings. When applied to navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences consisting of repeats and mixtures of slow forward and turn bouts. Applied to a novel chemotaxis assay, BASS uncovers chemotactic strategies deployed by zebrafish to avoid aversive cues consisting of sequences of fast large-angle turns and burst swims. In a simulated dataset of soaring gliders climbing thermals, BASS finds the spiraling patterns characteristic of soaring behaviour. In both cases, BASS succeeds in identifying rare action sequences in the behaviour deployed by freely moving animals. BASS can be easily incorporated into the pipelines of existing behavioural analyses across diverse species, and even more broadly used as a generic algorithm for pattern recognition in low-dimensional sequential data.
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Affiliation(s)
- Gautam Reddy
- NSF-Simons Center for Mathematical & Statistical Analysis of Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Laura Desban
- Sorbonne Université, Institut du Cerveau (ICM), Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Hidenori Tanaka
- Physics & Informatics Laboratories, NTT Research, Inc., East Palo Alto, California, United States of America
- Department of Applied Physics, Stanford University, Stanford, California, United States of America
| | - Julian Roussel
- Sorbonne Université, Institut du Cerveau (ICM), Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Olivier Mirat
- Sorbonne Université, Institut du Cerveau (ICM), Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Claire Wyart
- Sorbonne Université, Institut du Cerveau (ICM), Inserm U 1127, CNRS UMR 7225, Paris, France
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44
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Sainburg T, Gentner TQ. Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions. Front Behav Neurosci 2021; 15:811737. [PMID: 34987365 PMCID: PMC8721140 DOI: 10.3389/fnbeh.2021.811737] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 11/29/2021] [Indexed: 11/23/2022] Open
Abstract
Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.
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Affiliation(s)
- Tim Sainburg
- Department of Psychology, University of California, San Diego, La Jolla, CA, United States
- Center for Academic Research & Training in Anthropogeny, University of California, San Diego, La Jolla, CA, United States
| | - Timothy Q. Gentner
- Department of Psychology, University of California, San Diego, La Jolla, CA, United States
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, United States
- Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, United States
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45
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Draelos A, Gupta P, Jun NY, Sriworarat C, Pearson J. Bubblewrap: Online tiling and real-time flow prediction on neural manifolds. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 2021; 34:6062-6074. [PMID: 35785106 PMCID: PMC9247712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
While most classic studies of function in experimental neuroscience have focused on the coding properties of individual neurons, recent developments in recording technologies have resulted in an increasing emphasis on the dynamics of neural populations. This has given rise to a wide variety of models for analyzing population activity in relation to experimental variables, but direct testing of many neural population hypotheses requires intervening in the system based on current neural state, necessitating models capable of inferring neural state online. Existing approaches, primarily based on dynamical systems, require strong parametric assumptions that are easily violated in the noise-dominated regime and do not scale well to the thousands of data channels in modern experiments. To address this problem, we propose a method that combines fast, stable dimensionality reduction with a soft tiling of the resulting neural manifold, allowing dynamics to be approximated as a probability flow between tiles. This method can be fit efficiently using online expectation maximization, scales to tens of thousands of tiles, and outperforms existing methods when dynamics are noise-dominated or feature multi-modal transition probabilities. The resulting model can be trained at kiloHertz data rates, produces accurate approximations of neural dynamics within minutes, and generates predictions on submillisecond time scales. It retains predictive performance throughout many time steps into the future and is fast enough to serve as a component of closed-loop causal experiments.
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Affiliation(s)
| | | | | | | | - John Pearson
- Biostatistics & Bioinformatics, Electrical & Computer Engineering, Neurobiology, Psychology & Neuroscience, Duke University
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46
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Traner M, Chandak R, Raman B. Recent approaches to study the neural bases of complex insect behavior. CURRENT OPINION IN INSECT SCIENCE 2021; 48:18-25. [PMID: 34380094 DOI: 10.1016/j.cois.2021.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 07/26/2021] [Accepted: 07/28/2021] [Indexed: 06/13/2023]
Abstract
Recent advances in biocompatible materials, miniaturized instrumentation, advanced computational algorithms, and genetic tools have enabled the development of novel methods and approaches to quantify the behavior of individuals or groups of animals. In conjunction with technologies that allow simultaneous monitoring of neural responses, quantitative studies of complex behaviors can reveal tighter links between the external sensory cues in the vicinity of the organism and neural responses they elicit, and how internal neural representations finally get mapped onto the behavior generated. In this review, we examine a few approaches that are beginning to be widely exploited for understanding neural-behavioral response transformations.
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Affiliation(s)
- Michael Traner
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, Campus Box 1097, St. Louis, MO 63130, United States
| | - Rishabh Chandak
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, Campus Box 1097, St. Louis, MO 63130, United States
| | - Baranidharan Raman
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, Campus Box 1097, St. Louis, MO 63130, United States.
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47
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Eriksson D, Heiland M, Schneider A, Diester I. Distinct dynamics of neuronal activity during concurrent motor planning and execution. Nat Commun 2021; 12:5390. [PMID: 34508073 PMCID: PMC8433382 DOI: 10.1038/s41467-021-25558-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 08/11/2021] [Indexed: 11/09/2022] Open
Abstract
The smooth conduct of movements requires simultaneous motor planning and execution according to internal goals. So far it remains unknown how such movement plans are modified without interfering with ongoing movements. Previous studies have isolated planning and execution-related neuronal activity by separating behavioral planning and movement periods in time by sensory cues. Here, we separate continuous self-paced motor planning from motor execution statistically, by experimentally minimizing the repetitiveness of the movements. This approach shows that, in the rat sensorimotor cortex, neuronal motor planning processes evolve with slower dynamics than movement-related responses. Fast-evolving neuronal activity precees skilled forelimb movements and is nested within slower dynamics. We capture this effect via high-pass filtering and confirm the results with optogenetic stimulations. The various dynamics combined with adaptation-based high-pass filtering provide a simple principle for separating concurrent motor planning and execution.
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Affiliation(s)
- David Eriksson
- Optophysiology, University of Freiburg, Faculty of Biology, Freiburg, Germany.
| | - Mona Heiland
- Optophysiology, University of Freiburg, Faculty of Biology, Freiburg, Germany.,Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland
- RCSI, Dublin 2, Ireland
| | - Artur Schneider
- Optophysiology, University of Freiburg, Faculty of Biology, Freiburg, Germany.,BrainLinks-BrainTools, Intelligent Machine-Brain Interfacing Technology (IMBIT), University of Freiburg, Freiburg, Germany
| | - Ilka Diester
- Optophysiology, University of Freiburg, Faculty of Biology, Freiburg, Germany. .,BrainLinks-BrainTools, Intelligent Machine-Brain Interfacing Technology (IMBIT), University of Freiburg, Freiburg, Germany. .,Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany.
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48
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McCullough MH, Goodhill GJ. Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain. Curr Opin Neurobiol 2021; 70:89-100. [PMID: 34482006 DOI: 10.1016/j.conb.2021.07.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/20/2021] [Accepted: 07/21/2021] [Indexed: 01/02/2023]
Abstract
Neural computation has evolved to optimize the behaviors that enable our survival. Although much previous work in neuroscience has focused on constrained task behaviors, recent advances in computer vision are fueling a trend toward the study of naturalistic behaviors. Automated tracking of fine-scale behaviors is generating rich datasets for animal models including rodents, fruit flies, zebrafish, and worms. However, extracting meaning from these large and complex data often requires sophisticated computational techniques. Here we review the latest methods and modeling approaches providing new insights into the brain from behavior. We focus on unsupervised methods for identifying stereotyped behaviors and for resolving details of the structure and dynamics of behavioral sequences.
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Affiliation(s)
- Michael H McCullough
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Geoffrey J Goodhill
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia; School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, 4072, Australia.
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49
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Hernández DG, Rivera C, Cande J, Zhou B, Stern DL, Berman GJ. A framework for studying behavioral evolution by reconstructing ancestral repertoires. eLife 2021; 10:e61806. [PMID: 34473052 PMCID: PMC8445618 DOI: 10.7554/elife.61806] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 09/01/2021] [Indexed: 11/16/2022] Open
Abstract
Although different animal species often exhibit extensive variation in many behaviors, typically scientists examine one or a small number of behaviors in any single study. Here, we propose a new framework to simultaneously study the evolution of many behaviors. We measured the behavioral repertoire of individuals from six species of fruit flies using unsupervised techniques and identified all stereotyped movements exhibited by each species. We then fit a Generalized Linear Mixed Model to estimate the intra- and inter-species behavioral covariances, and, by using the known phylogenetic relationships among species, we estimated the (unobserved) behaviors exhibited by ancestral species. We found that much of intra-specific behavioral variation has a similar covariance structure to previously described long-time scale variation in an individual's behavior, suggesting that much of the measured variation between individuals of a single species in our assay reflects differences in the status of neural networks, rather than genetic or developmental differences between individuals. We then propose a method to identify groups of behaviors that appear to have evolved in a correlated manner, illustrating how sets of behaviors, rather than individual behaviors, likely evolved. Our approach provides a new framework for identifying co-evolving behaviors and may provide new opportunities to study the mechanistic basis of behavioral evolution.
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Affiliation(s)
- Damián G Hernández
- Department of Physics, Emory UniversityAtlantaUnited States
- Department of Medical Physics, Centro Atómico Bariloche and Instituto BalseiroBarilocheArgentina
| | | | - Jessica Cande
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Baohua Zhou
- Department of Physics, Emory UniversityAtlantaUnited States
- Department of Molecular, Cellular and Developmental Biology, Yale UniversityNew HavenUnited States
| | - David L Stern
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gordon J Berman
- Department of Physics, Emory UniversityAtlantaUnited States
- Department of Biology, Emory UniversityAtlantaUnited States
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50
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Hsu AI, Yttri EA. B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors. Nat Commun 2021; 12:5188. [PMID: 34465784 PMCID: PMC8408193 DOI: 10.1038/s41467-021-25420-x] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
Studying naturalistic animal behavior remains a difficult objective. Recent machine learning advances have enabled limb localization; however, extracting behaviors requires ascertaining the spatiotemporal patterns of these positions. To provide a link from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in any animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of rodent and other models of pain, OCD, and movement disorders.
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Affiliation(s)
- Alexander I Hsu
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Eric A Yttri
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
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