51
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Han Y, Ziebell P, Riccio A, Halder S. Two sides of the same coin: adaptation of BCIs to internal states with user-centered design and electrophysiological features. BRAIN-COMPUTER INTERFACES 2022. [DOI: 10.1080/2326263x.2022.2041294] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Yiyuan Han
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Philipp Ziebell
- Institute of Psychology, University of Würzburg, Würzburg, Germany
| | - Angela Riccio
- Neuroelectrical Imaging and Brain Computer Interface Laboratory,Fondazione Santa Lucia, Irccs, Rome, Italy
| | - Sebastian Halder
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
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52
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Bolkan SS, Stone IR, Pinto L, Ashwood ZC, Iravedra Garcia JM, Herman AL, Singh P, Bandi A, Cox J, Zimmerman CA, Cho JR, Engelhard B, Pillow JW, Witten IB. Opponent control of behavior by dorsomedial striatal pathways depends on task demands and internal state. Nat Neurosci 2022; 25:345-357. [PMID: 35260863 PMCID: PMC8915388 DOI: 10.1038/s41593-022-01021-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 01/21/2022] [Indexed: 11/27/2022]
Abstract
A classic view of the striatum holds that activity in direct and indirect pathways oppositely modulates motor output. Whether this involves direct control of movement, or reflects a cognitive process underlying movement, remains unresolved. Here we find that strong, opponent control of behavior by the two pathways of the dorsomedial striatum depends on the cognitive requirements of a task. Furthermore, a latent state model (a hidden Markov model with generalized linear model observations) reveals that-even within a single task-the contribution of the two pathways to behavior is state dependent. Specifically, the two pathways have large contributions in one of two states associated with a strategy of evidence accumulation, compared to a state associated with a strategy of repeating previous choices. Thus, both the demands imposed by a task, as well as the internal state of mice when performing a task, determine whether dorsomedial striatum pathways provide strong and opponent control of behavior.
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Affiliation(s)
- Scott S Bolkan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Iris R Stone
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Lucas Pinto
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Zoe C Ashwood
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Alison L Herman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Priyanka Singh
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Akhil Bandi
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Julia Cox
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Jounhong Ryan Cho
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ben Engelhard
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Department of Psychology, Princeton University, Princeton, NJ, USA.
| | - Ilana B Witten
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Department of Psychology, Princeton University, Princeton, NJ, USA.
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53
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Cortical ensembles orchestrate social competition through hypothalamic outputs. Nature 2022; 603:667-671. [PMID: 35296862 PMCID: PMC9576144 DOI: 10.1038/s41586-022-04507-5] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 02/02/2022] [Indexed: 01/27/2023]
Abstract
Most social species self-organize into dominance hierarchies1,2, which decreases aggression and conserves energy3,4, but it is not clear how individuals know their social rank. We have only begun to learn how the brain represents social rank5-9 and guides behaviour on the basis of this representation. The medial prefrontal cortex (mPFC) is involved in social dominance in rodents7,8 and humans10,11. Yet, precisely how the mPFC encodes relative social rank and which circuits mediate this computation is not known. We developed a social competition assay in which mice compete for rewards, as well as a computer vision tool (AlphaTracker) to track multiple, unmarked animals. A hidden Markov model combined with generalized linear models was able to decode social competition behaviour from mPFC ensemble activity. Population dynamics in the mPFC predicted social rank and competitive success. Finally, we demonstrate that mPFC cells that project to the lateral hypothalamus promote dominance behaviour during reward competition. Thus, we reveal a cortico-hypothalamic circuit by which the mPFC exerts top-down modulation of social dominance.
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54
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Robson DN, Li JM. A dynamical systems view of neuroethology: Uncovering stateful computation in natural behaviors. Curr Opin Neurobiol 2022; 73:102517. [PMID: 35217311 DOI: 10.1016/j.conb.2022.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 01/06/2022] [Accepted: 01/11/2022] [Indexed: 11/03/2022]
Abstract
State-dependent computation is key to cognition in both biological and artificial systems. Alan Turing recognized the power of stateful computation when he created the Turing machine with theoretically infinite computational capacity in 1936. Independently, by 1950, ethologists such as Tinbergen and Lorenz also began to implicitly embed rudimentary forms of state-dependent computation to create qualitative models of internal drives and naturally occurring animal behaviors. Here, we reformulate core ethological concepts in explicitly dynamical systems terms for stateful computation. We examine, based on a wealth of recent neural data collected during complex innate behaviors across species, the neural dynamics that determine the temporal structure of internal states. We will also discuss the degree to which the brain can be hierarchically partitioned into nested dynamical systems and the need for a multi-dimensional state-space model of the neuromodulatory system that underlies motivational and affective states.
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Affiliation(s)
- Drew N Robson
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.
| | - Jennifer M Li
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.
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55
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Histed MH, O’Rawe JF. From choices to internal states. Nat Neurosci 2022; 25:138-139. [DOI: 10.1038/s41593-021-01008-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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56
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Ning J, Li Z, Zhang X, Wang J, Chen D, Liu Q, Sun Y. Behavioral signatures of structured feature detection during courtship in Drosophila. Curr Biol 2022; 32:1211-1231.e7. [DOI: 10.1016/j.cub.2022.01.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 11/27/2021] [Accepted: 01/10/2022] [Indexed: 11/27/2022]
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57
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Ashwood ZC, Roy NA, Stone IR, Urai AE, Churchland AK, Pouget A, Pillow JW. Mice alternate between discrete strategies during perceptual decision-making. Nat Neurosci 2022; 25:201-212. [PMID: 35132235 PMCID: PMC8890994 DOI: 10.1038/s41593-021-01007-z] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 12/17/2021] [Indexed: 12/21/2022]
Abstract
Classical models of perceptual decision-making assume that subjects use a single, consistent strategy to form decisions, or that decision-making strategies evolve slowly over time. Here we present new analyses suggesting that this common view is incorrect. We analyzed data from mouse and human decision-making experiments and found that choice behavior relies on an interplay among multiple interleaved strategies. These strategies, characterized by states in a hidden Markov model, persist for tens to hundreds of trials before switching, and often switch multiple times within a session. The identified decision-making strategies were highly consistent across mice and comprised a single 'engaged' state, in which decisions relied heavily on the sensory stimulus, and several biased states in which errors frequently occurred. These results provide a powerful alternate explanation for 'lapses' often observed in rodent behavioral experiments, and suggest that standard measures of performance mask the presence of major changes in strategy across trials.
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Affiliation(s)
- Zoe C Ashwood
- Deptartment of Computer Science, Princeton University, Princeton, NJ, USA.
- Princeton Neuroscience Institute, Princeton, NJ, USA.
| | | | - Iris R Stone
- Princeton Neuroscience Institute, Princeton, NJ, USA
| | - Anne E Urai
- Cognitive Psychology Unit, Leiden University, Leiden, Netherlands
| | - Anne K Churchland
- David Geffen School of Medicine, The University of California, Los Angeles, Los Angeles, CA, USA
| | - Alexandre Pouget
- Faculty of Medicine & Deptartment of Basic Neurosciences, University of Geneva, Geneva, Switzerland
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton, NJ, USA.
- Department of Psychology, Princeton University, Princeton, NJ, USA.
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58
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Devineni AV, Scaplen KM. Neural Circuits Underlying Behavioral Flexibility: Insights From Drosophila. Front Behav Neurosci 2022; 15:821680. [PMID: 35069145 PMCID: PMC8770416 DOI: 10.3389/fnbeh.2021.821680] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 12/14/2021] [Indexed: 11/16/2022] Open
Abstract
Behavioral flexibility is critical to survival. Animals must adapt their behavioral responses based on changes in the environmental context, internal state, or experience. Studies in Drosophila melanogaster have provided insight into the neural circuit mechanisms underlying behavioral flexibility. Here we discuss how Drosophila behavior is modulated by internal and behavioral state, environmental context, and learning. We describe general principles of neural circuit organization and modulation that underlie behavioral flexibility, principles that are likely to extend to other species.
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Affiliation(s)
- Anita V. Devineni
- Department of Biology, Emory University, Atlanta, GA, United States
- Zuckerman Mind Brain Institute, Columbia University, New York, NY, United States
| | - Kristin M. Scaplen
- Department of Psychology, Bryant University, Smithfield, RI, United States
- Center for Health and Behavioral Studies, Bryant University, Smithfield, RI, United States
- Department of Neuroscience, Brown University, Providence, RI, United States
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59
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Urai AE, Doiron B, Leifer AM, Churchland AK. Large-scale neural recordings call for new insights to link brain and behavior. Nat Neurosci 2022; 25:11-19. [PMID: 34980926 DOI: 10.1038/s41593-021-00980-9] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/08/2021] [Indexed: 12/17/2022]
Abstract
Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.
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Affiliation(s)
- Anne E Urai
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.,Cognitive Psychology Unit, Leiden University, Leiden, The Netherlands
| | | | | | - Anne K Churchland
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. .,University of California Los Angeles, Los Angeles, CA, USA.
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60
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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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61
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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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62
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Distinct movement patterns generate stages of spider web building. Curr Biol 2021; 31:4983-4997.e5. [PMID: 34619095 DOI: 10.1016/j.cub.2021.09.030] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/05/2021] [Accepted: 09/14/2021] [Indexed: 11/20/2022]
Abstract
The geometric complexity and stereotypy of spider webs have long generated interest in their algorithmic origin. Like other examples of animal architecture, web construction is the result of several assembly phases that are driven by distinct behavioral stages coordinated to build a successful structure. Manual observations have revealed a range of sensory cues and movement patterns used during web construction, but methods to systematically quantify the dynamics of these sensorimotor patterns are lacking. Here, we apply an analytical pipeline to quantify web-making behavior of the orb-weaver Uloborus diversus. Position tracking revealed stereotyped stages of construction that could occur in typical or atypical progressions across individuals. Using an unsupervised clustering approach, we identified general and stage-specific leg movements. A hierarchical hidden Markov model revealed that web-building stages are characterized by stereotyped sequences of actions largely shared across individuals, regardless of whether these stages progress in a typical or an atypical fashion. Web stages could be predicted based on action sequences alone, revealing that web-stage geometries are a physical manifestation of behavioral transition regimes.
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63
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Steinfath E, Palacios-Muñoz A, Rottschäfer JR, Yuezak D, Clemens J. Fast and accurate annotation of acoustic signals with deep neural networks. eLife 2021; 10:e68837. [PMID: 34723794 PMCID: PMC8560090 DOI: 10.7554/elife.68837] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 10/04/2021] [Indexed: 01/06/2023] Open
Abstract
Acoustic signals serve communication within and across species throughout the animal kingdom. Studying the genetics, evolution, and neurobiology of acoustic communication requires annotating acoustic signals: segmenting and identifying individual acoustic elements like syllables or sound pulses. To be useful, annotations need to be accurate, robust to noise, and fast. We here introduce DeepAudioSegmenter (DAS), a method that annotates acoustic signals across species based on a deep-learning derived hierarchical presentation of sound. We demonstrate the accuracy, robustness, and speed of DAS using acoustic signals with diverse characteristics from insects, birds, and mammals. DAS comes with a graphical user interface for annotating song, training the network, and for generating and proofreading annotations. The method can be trained to annotate signals from new species with little manual annotation and can be combined with unsupervised methods to discover novel signal types. DAS annotates song with high throughput and low latency for experimental interventions in realtime. Overall, DAS is a universal, versatile, and accessible tool for annotating acoustic communication signals.
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Affiliation(s)
- Elsa Steinfath
- European Neuroscience Institute - A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of GöttingenGöttingenGermany
| | - Adrian Palacios-Muñoz
- European Neuroscience Institute - A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of GöttingenGöttingenGermany
| | - Julian R Rottschäfer
- European Neuroscience Institute - A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of GöttingenGöttingenGermany
| | - Deniz Yuezak
- European Neuroscience Institute - A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of GöttingenGöttingenGermany
| | - Jan Clemens
- European Neuroscience Institute - A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- Bernstein Center for Computational NeuroscienceGöttingenGermany
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64
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Werkhoven Z, Bravin A, Skutt-Kakaria K, Reimers P, Pallares LF, Ayroles J, de Bivort BL. The structure of behavioral variation within a genotype. eLife 2021; 10:64988. [PMID: 34664550 PMCID: PMC8526060 DOI: 10.7554/elife.64988] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 09/14/2021] [Indexed: 12/13/2022] Open
Abstract
Individual animals vary in their behaviors. This is true even when they share the same genotype and were reared in the same environment. Clusters of covarying behaviors constitute behavioral syndromes, and an individual’s position along such axes of covariation is a representation of their personality. Despite these conceptual frameworks, the structure of behavioral covariation within a genotype is essentially uncharacterized and its mechanistic origins unknown. Passing hundreds of inbred Drosophila individuals through an experimental pipeline that captured hundreds of behavioral measures, we found sparse but significant correlations among small sets of behaviors. Thus, the space of behavioral variation has many independent dimensions. Manipulating the physiology of the brain, and specific neural populations, altered specific correlations. We also observed that variation in gene expression can predict an individual’s position on some behavioral axes. This work represents the first steps in understanding the biological mechanisms determining the structure of behavioral variation within a genotype.
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Affiliation(s)
- Zachary Werkhoven
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States
| | - Alyssa Bravin
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States
| | - Kyobi Skutt-Kakaria
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States.,Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
| | - Pablo Reimers
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States.,Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Luisa F Pallares
- Department of Ecology and Evolutionary Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States
| | - Julien Ayroles
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States.,Department of Ecology and Evolutionary Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States
| | - Benjamin L de Bivort
- Center for Brain Science and Department of Organismic and Evolutionary Biology, Cambridge, United States
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65
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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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66
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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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67
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Hein AM, Altshuler DL, Cade DE, Liao JC, Martin BT, Taylor GK. An Algorithmic Approach to Natural Behavior. Curr Biol 2021; 30:R663-R675. [PMID: 32516620 DOI: 10.1016/j.cub.2020.04.018] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Uncovering the mechanisms and implications of natural behavior is a goal that unites many fields of biology. Yet, the diversity, flexibility, and multi-scale nature of these behaviors often make understanding elusive. Here, we review studies of animal pursuit and evasion - two special classes of behavior where theory-driven experiments and new modeling techniques are beginning to uncover the general control principles underlying natural behavior. A key finding of these studies is that intricate sequences of pursuit and evasion behavior can often be constructed through simple, repeatable rules that link sensory input to motor output: we refer to these rules as behavioral algorithms. Identifying and mathematically characterizing these algorithms has led to important insights, including the discovery of guidance rules that attacking predators use to intercept mobile prey, and coordinated neural and biomechanical mechanisms that animals use to avoid impending collisions. Here, we argue that algorithms provide a good starting point for studies of natural behavior more generally. Rather than beginning at the neural or ecological levels of organization, we advocate starting in the middle, where the algorithms that link sensory input to behavioral output can provide a solid foundation from which to explore both the implementation and the ecological outcomes of behavior. We review insights that have been gained through such an algorithmic approach to pursuit and evasion behaviors. From these, we synthesize theoretical principles and lay out key modeling tools needed to apply an algorithmic approach to the study of other complex natural behaviors.
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Affiliation(s)
- Andrew M Hein
- Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Santa Cruz, CA 95060, USA; Institute of Marine Sciences, University of California, Santa Cruz, CA 95060, USA; Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95060, USA.
| | - Douglas L Altshuler
- Department of Zoology, University of British Columbia, Vancouver, BC V6T1Z4, Canada
| | - David E Cade
- Institute of Marine Sciences, University of California, Santa Cruz, CA 95060, USA; Hopkins Marine Station, Department of Biology, Stanford University, Pacific Grove, CA 93950, USA
| | - James C Liao
- The Whitney Laboratory for Marine Bioscience, Department of Biology, University of Florida, 9505 Ocean Shore Blvd., St. Augustine, FL 32080, USA
| | - Benjamin T Martin
- Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Santa Cruz, CA 95060, USA; Institute of Marine Sciences, University of California, Santa Cruz, CA 95060, USA; Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
| | - Graham K Taylor
- Department of Zoology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, UK
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68
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Low IIC, Williams AH, Campbell MG, Linderman SW, Giocomo LM. Dynamic and reversible remapping of network representations in an unchanging environment. Neuron 2021; 109:2967-2980.e11. [PMID: 34363753 DOI: 10.1016/j.neuron.2021.07.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/26/2021] [Accepted: 07/06/2021] [Indexed: 12/14/2022]
Abstract
Neurons in the medial entorhinal cortex alter their firing properties in response to environmental changes. This flexibility in neural coding is hypothesized to support navigation and memory by dividing sensory experience into unique episodes. However, it is unknown how the entorhinal circuit as a whole transitions between different representations when sensory information is not delineated into discrete contexts. Here we describe rapid and reversible transitions between multiple spatial maps of an unchanging task and environment. These remapping events were synchronized across hundreds of neurons, differentially affected navigational cell types, and correlated with changes in running speed. Despite widespread changes in spatial coding, remapping comprised a translation along a single dimension in population-level activity space, enabling simple decoding strategies. These findings provoke reconsideration of how the medial entorhinal cortex dynamically represents space and suggest a remarkable capacity of cortical circuits to rapidly and substantially reorganize their neural representations.
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Affiliation(s)
- Isabel I C Low
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Alex H Williams
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University, Stanford, CA, USA
| | - Malcolm G Campbell
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Scott W Linderman
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University, Stanford, CA, USA
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
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69
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Mobbs D, Wise T, Suthana N, Guzmán N, Kriegeskorte N, Leibo JZ. Promises and challenges of human computational ethology. Neuron 2021; 109:2224-2238. [PMID: 34143951 PMCID: PMC8769712 DOI: 10.1016/j.neuron.2021.05.021] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/05/2021] [Accepted: 05/17/2021] [Indexed: 12/22/2022]
Abstract
The movements an organism makes provide insights into its internal states and motives. This principle is the foundation of the new field of computational ethology, which links rich automatic measurements of natural behaviors to motivational states and neural activity. Computational ethology has proven transformative for animal behavioral neuroscience. This success raises the question of whether rich automatic measurements of behavior can similarly drive progress in human neuroscience and psychology. New technologies for capturing and analyzing complex behaviors in real and virtual environments enable us to probe the human brain during naturalistic dynamic interactions with the environment that so far were beyond experimental investigation. Inspired by nonhuman computational ethology, we explore how these new tools can be used to test important questions in human neuroscience. We argue that application of this methodology will help human neuroscience and psychology extend limited behavioral measurements such as reaction time and accuracy, permit novel insights into how the human brain produces behavior, and ultimately reduce the growing measurement gap between human and animal neuroscience.
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Affiliation(s)
- Dean Mobbs
- Department of Humanities and Social Sciences, 1200 E. California Blvd., HSS 228-77, Pasadena, CA 91125, USA; Computation and Neural Systems Program at the California Institute of Technology, 1200 E. California Blvd., HSS 228-77, Pasadena, CA 91125, USA.
| | - Toby Wise
- Department of Humanities and Social Sciences, 1200 E. California Blvd., HSS 228-77, Pasadena, CA 91125, USA; Wellcome Centre for Human Neuroimaging, University College London, London, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA; Departments of Neurosurgery, Psychology, and Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noah Guzmán
- Computation and Neural Systems Program at the California Institute of Technology, 1200 E. California Blvd., HSS 228-77, Pasadena, CA 91125, USA
| | - Nikolaus Kriegeskorte
- Department of Psychology, Columbia University, New York, NY, USA; Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
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70
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Sun JJ, Kennedy A, Zhan E, Anderson DJ, Yue Y, Perona P. Task Programming: Learning Data Efficient Behavior Representations. PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2021; 2021:2875-2884. [PMID: 36544482 PMCID: PMC9766046 DOI: 10.1109/cvpr46437.2021.00290] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video tracking data. To reduce annotation effort, we present TREBA: a method to learn annotation-sample efficient trajectory embedding for behavior analysis, based on multi-task self-supervised learning. The tasks in our method can be efficiently engineered by domain experts through a process we call "task programming", which uses programs to explicitly encode structured knowledge from domain experts. Total domain expert effort can be reduced by exchanging data annotation time for the construction of a small number of programmed tasks. We evaluate this trade-off using data from behavioral neuroscience, in which specialized domain knowledge is used to identify behaviors. We present experimental results in three datasets across two domains: mice and fruit flies. Using embeddings from TREBA, we reduce annotation burden by up to a factor of 10 without compromising accuracy compared to state-of-the-art features. Our results thus suggest that task programming and self-supervision can be an effective way to reduce annotation effort for domain experts.
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71
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Langdon AJ, Chaudhuri R. An evolving perspective on the dynamic brain: Notes from the Brain Conference on Dynamics of the brain: Temporal aspects of computation. Eur J Neurosci 2021; 53:3511-3524. [PMID: 32896026 PMCID: PMC7946155 DOI: 10.1111/ejn.14963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/15/2020] [Accepted: 08/26/2020] [Indexed: 11/29/2022]
Affiliation(s)
- Angela J. Langdon
- Princeton Neuroscience Institute & Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Rishidev Chaudhuri
- Center for Neuroscience, Department of Mathematics and Department of Neurobiology, Physiology & Behavior, University of California, Davis, Davis CA, USA
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72
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Ebbesen CL, Froemke RC. Body language signals for rodent social communication. Curr Opin Neurobiol 2021; 68:91-106. [PMID: 33582455 PMCID: PMC8243782 DOI: 10.1016/j.conb.2021.01.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 01/09/2021] [Accepted: 01/25/2021] [Indexed: 12/15/2022]
Abstract
Integration of social cues to initiate adaptive emotional and behavioral responses is a fundamental aspect of animal and human behavior. In humans, social communication includes prominent nonverbal components, such as social touch, gestures and facial expressions. Comparative studies investigating the neural basis of social communication in rodents has historically been centered on olfactory signals and vocalizations, with relatively less focus on non-verbal social cues. Here, we outline two exciting research directions: First, we will review recent observations pointing to a role of social facial expressions in rodents. Second, we will review observations that point to a role of 'non-canonical' rodent body language: body posture signals beyond stereotyped displays in aggressive and sexual behavior. In both sections, we will outline how social neuroscience can build on recent advances in machine learning, robotics and micro-engineering to push these research directions forward towards a holistic systems neurobiology of rodent body language.
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Affiliation(s)
- Christian L Ebbesen
- Skirball Institute of Biomolecular Medicine, Neuroscience Institute, Departments of Otolaryngology, 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, Neuroscience Institute, Departments of Otolaryngology, 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; Howard Hughes Medical Institute Faculty Scholar, USA.
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73
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Key B, Zalucki O, Brown DJ. Neural Design Principles for Subjective Experience: Implications for Insects. Front Behav Neurosci 2021; 15:658037. [PMID: 34025371 PMCID: PMC8131515 DOI: 10.3389/fnbeh.2021.658037] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 04/07/2021] [Indexed: 02/04/2023] Open
Abstract
How subjective experience is realized in nervous systems remains one of the great challenges in the natural sciences. An answer to this question should resolve debate about which animals are capable of subjective experience. We contend that subjective experience of sensory stimuli is dependent on the brain's awareness of its internal neural processing of these stimuli. This premise is supported by empirical evidence demonstrating that disruption to either processing streams or awareness states perturb subjective experience. Given that the brain must predict the nature of sensory stimuli, we reason that conscious awareness is itself dependent on predictions generated by hierarchically organized forward models of the organism's internal sensory processing. The operation of these forward models requires a specialized neural architecture and hence any nervous system lacking this architecture is unable to subjectively experience sensory stimuli. This approach removes difficulties associated with extrapolations from behavioral and brain homologies typically employed in addressing whether an animal can feel. Using nociception as a model sensation, we show here that the Drosophila brain lacks the required internal neural connectivity to implement the computations required of hierarchical forward models. Consequently, we conclude that Drosophila, and those insects with similar neuroanatomy, do not subjectively experience noxious stimuli and therefore cannot feel pain.
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Affiliation(s)
- Brian Key
- School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Oressia Zalucki
- School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Deborah J. Brown
- School of Historical and Philosophical Inquiry, The University of Queensland, Brisbane, QLD, Australia
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74
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Marshall JD, Aldarondo DE, Dunn TW, Wang WL, Berman GJ, Ölveczky BP. Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire. Neuron 2021; 109:420-437.e8. [PMID: 33340448 PMCID: PMC7864892 DOI: 10.1016/j.neuron.2020.11.016] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 10/01/2020] [Accepted: 11/16/2020] [Indexed: 12/13/2022]
Abstract
In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat's head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10- to 100-fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking. We demonstrate CAPTURE's ability to comprehensively profile the kinematics and sequential organization of natural rodent behavior, its variation across individuals, and its perturbation by drugs and disease, including identifying perseverative grooming states in a rat model of fragile X syndrome. CAPTURE significantly expands the range of behaviors and contexts that can be quantitatively investigated, opening the door to a new understanding of natural behavior and its neural basis.
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Affiliation(s)
- Jesse D Marshall
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.
| | - Diego E Aldarondo
- Program in Neuroscience, Harvard University, Cambridge, MA 02138, USA
| | - Timothy W Dunn
- Department of Statistical Science, Duke University, Durham, NC 27710, USA
| | - William L Wang
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Gordon J Berman
- Department of Biology, Emory University, Atlanta, GA 30322, USA
| | - Bence P Ölveczky
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.
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75
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Leng X, Wohl M, Ishii K, Nayak P, Asahina K. Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm. PLoS One 2020; 15:e0241696. [PMID: 33326445 PMCID: PMC7743940 DOI: 10.1371/journal.pone.0241696] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 10/16/2020] [Indexed: 11/22/2022] Open
Abstract
Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.
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Affiliation(s)
- Xubo Leng
- Salk Institute for Biological Studies, La Jolla, California, United States of America
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, California, United States of America
| | - Margot Wohl
- Salk Institute for Biological Studies, La Jolla, California, United States of America
- Neuroscience Graduate Program, University of California, San Diego, La Jolla, California, United States of America
| | - Kenichi Ishii
- Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Pavan Nayak
- Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Kenta Asahina
- Salk Institute for Biological Studies, La Jolla, California, United States of America
- * E-mail:
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76
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Pereira TD, Shaevitz JW, Murthy M. Quantifying behavior to understand the brain. Nat Neurosci 2020; 23:1537-1549. [PMID: 33169033 PMCID: PMC7780298 DOI: 10.1038/s41593-020-00734-z] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 10/02/2020] [Indexed: 02/07/2023]
Abstract
Over the past years, numerous methods have emerged to automate the quantification of animal behavior at a resolution not previously imaginable. This has opened up a new field of computational ethology and will, in the near future, make it possible to quantify in near completeness what an animal is doing as it navigates its environment. The importance of improving the techniques with which we characterize behavior is reflected in the emerging recognition that understanding behavior is an essential (or even prerequisite) step to pursuing neuroscience questions. The use of these methods, however, is not limited to studying behavior in the wild or in strictly ethological settings. Modern tools for behavioral quantification can be applied to the full gamut of approaches that have historically been used to link brain to behavior, from psychophysics to cognitive tasks, augmenting those measurements with rich descriptions of how animals navigate those tasks. Here we review recent technical advances in quantifying behavior, particularly in methods for tracking animal motion and characterizing the structure of those dynamics. We discuss open challenges that remain for behavioral quantification and highlight promising future directions, with a strong emphasis on emerging approaches in deep learning, the core technology that has enabled the markedly rapid pace of progress of this field. We then discuss how quantitative descriptions of behavior can be leveraged to connect brain activity with animal movements, with the ultimate goal of resolving the relationship between neural circuits, cognitive processes and behavior.
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Affiliation(s)
- Talmo D Pereira
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Joshua W Shaevitz
- Department of Physics, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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77
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Deutsch D, Pacheco D, Encarnacion-Rivera L, Pereira T, Fathy R, Clemens J, Girardin C, Calhoun A, Ireland E, Burke A, Dorkenwald S, McKellar C, Macrina T, Lu R, Lee K, Kemnitz N, Ih D, Castro M, Halageri A, Jordan C, Silversmith W, Wu J, Seung HS, Murthy M. The neural basis for a persistent internal state in Drosophila females. eLife 2020; 9:e59502. [PMID: 33225998 PMCID: PMC7787663 DOI: 10.7554/elife.59502] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 11/18/2020] [Indexed: 12/13/2022] Open
Abstract
Sustained changes in mood or action require persistent changes in neural activity, but it has been difficult to identify the neural circuit mechanisms that underlie persistent activity and contribute to long-lasting changes in behavior. Here, we show that a subset of Doublesex+ pC1 neurons in the Drosophila female brain, called pC1d/e, can drive minutes-long changes in female behavior in the presence of males. Using automated reconstruction of a volume electron microscopic (EM) image of the female brain, we map all inputs and outputs to both pC1d and pC1e. This reveals strong recurrent connectivity between, in particular, pC1d/e neurons and a specific subset of Fruitless+ neurons called aIPg. We additionally find that pC1d/e activation drives long-lasting persistent neural activity in brain areas and cells overlapping with the pC1d/e neural network, including both Doublesex+ and Fruitless+ neurons. Our work thus links minutes-long persistent changes in behavior with persistent neural activity and recurrent circuit architecture in the female brain.
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Affiliation(s)
- David Deutsch
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Diego Pacheco
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | | | - Talmo Pereira
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Ramie Fathy
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jan Clemens
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Cyrille Girardin
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Adam Calhoun
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Elise Ireland
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Austin Burke
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Department of Computer Science, Princeton UniversityPrincetonUnited States
| | - Claire McKellar
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Department of Computer Science, Princeton UniversityPrincetonUnited States
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Brain & Cognitive Science Department, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - William Silversmith
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Department of Computer Science, Princeton UniversityPrincetonUnited States
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
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78
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Cowley BR, Snyder AC, Acar K, Williamson RC, Yu BM, Smith MA. Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex. Neuron 2020; 108:551-567.e8. [PMID: 32810433 PMCID: PMC7822647 DOI: 10.1016/j.neuron.2020.07.021] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 06/15/2020] [Accepted: 07/17/2020] [Indexed: 12/22/2022]
Abstract
An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This state embodies multiple cognitive factors, such as arousal and fatigue, but it is unclear how these factors influence the neural processes that encode sensory stimuli and form a decision. We discovered that, unprompted by task conditions, animals slowly shifted their likelihood of detecting stimulus changes over the timescale of tens of minutes. Neural population activity from visual area V4, as well as from prefrontal cortex, slowly drifted together with these behavioral fluctuations. We found that this slow drift, rather than altering the encoding of the sensory stimulus, acted as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in population activity across multiple brain areas and sheds further light on how internal states contribute to the decision-making process.
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Affiliation(s)
- Benjamin R Cowley
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Adam C Snyder
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14642, USA; Department of Neuroscience, University of Rochester, Rochester, NY 14642, USA; Center for Visual Science, University of Rochester, Rochester, NY 14642, USA
| | - Katerina Acar
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Center for Neuroscience, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Ryan C Williamson
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA; University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Byron M Yu
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Matthew A Smith
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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79
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Tafazoli S, MacDowell CJ, Che Z, Letai KC, Steinhardt CR, Buschman TJ. Learning to control the brain through adaptive closed-loop patterned stimulation. J Neural Eng 2020; 17:056007. [PMID: 32927437 DOI: 10.1088/1741-2552/abb860] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVE Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed a model-free, adaptive, closed-loop stimulation (ACLS) system that learns to use multi-site electrical stimulation to control the pattern of activity of a population of neurons. APPROACH The ACLS system combined multi-electrode electrophysiological recordings with multi-site electrical stimulation to simultaneously record the activity of a population of 5-15 multiunit neurons and deliver spatially-patterned electrical stimulation across 4-16 sites. Using a closed-loop learning system, ACLS iteratively updated the pattern of stimulation to reduce the difference between the observed neural response and a specific target pattern of firing rates in the recorded multiunits. MAIN RESULTS In silico and in vivo experiments showed ACLS learns to produce specific patterns of neural activity (in ∼15 min) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus. SIGNIFICANCE Our results show an ACLS system that can learn, in real-time, to generate specific patterns of neural activity. This work provides a framework for using model-free closed-loop learning to control neural activity.
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Affiliation(s)
- Sina Tafazoli
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, United States of America. Lead contact and corresponding author
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80
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Seidenbecher SE, Sanders JI, von Philipsborn AC, Kvitsiani D. Reward foraging task and model-based analysis reveal how fruit flies learn value of available options. PLoS One 2020; 15:e0239616. [PMID: 33007023 PMCID: PMC7531776 DOI: 10.1371/journal.pone.0239616] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 09/10/2020] [Indexed: 11/18/2022] Open
Abstract
Foraging animals have to evaluate, compare and select food patches in order to increase their fitness. Understanding what drives foraging decisions requires careful manipulation of the value of alternative options while monitoring animals choices. Value-based decision-making tasks in combination with formal learning models have provided both an experimental and theoretical framework to study foraging decisions in lab settings. While these approaches were successfully used in the past to understand what drives choices in mammals, very little work has been done on fruit flies. This is despite the fact that fruit flies have served as model organism for many complex behavioural paradigms. To fill this gap we developed a single-animal, trial-based decision making task, where freely walking flies experienced optogenetic sugar-receptor neuron stimulation. We controlled the value of available options by manipulating the probabilities of optogenetic stimulation. We show that flies integrate reward history of chosen options and forget value of unchosen options. We further discover that flies assign higher values to rewards experienced early in the behavioural session, consistent with formal reinforcement learning models. Finally, we also show that the probabilistic rewards affect walking trajectories of flies, suggesting that accumulated value is controlling the navigation vector of flies in a graded fashion. These findings establish the fruit fly as a model organism to explore the genetic and circuit basis of reward foraging decisions.
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Affiliation(s)
- Sophie E Seidenbecher
- Danish Research Institute of Translational Neuroscience - DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Aarhus, Denmark.,Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Joshua I Sanders
- Danish Research Institute of Translational Neuroscience - DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Aarhus, Denmark.,Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Anne C von Philipsborn
- Danish Research Institute of Translational Neuroscience - DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Aarhus, Denmark.,Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Duda Kvitsiani
- Danish Research Institute of Translational Neuroscience - DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Aarhus, Denmark.,Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
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81
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Hol FJH, Lambrechts L, Prakash M. BiteOscope, an open platform to study mosquito biting behavior. eLife 2020; 9:e56829. [PMID: 32960173 PMCID: PMC7535929 DOI: 10.7554/elife.56829] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 09/05/2020] [Indexed: 01/16/2023] Open
Abstract
Female mosquitoes need a blood meal to reproduce, and in obtaining this essential nutrient they transmit deadly pathogens. Although crucial for the spread of mosquito-borne diseases, blood feeding remains poorly understood due to technological limitations. Indeed, studies often expose human subjects to assess biting behavior. Here, we present the biteOscope, a device that attracts mosquitoes to a host mimic which they bite to obtain an artificial blood meal. The host mimic is transparent, allowing high-resolution imaging of the feeding mosquito. Using machine learning, we extract detailed behavioral statistics describing the locomotion, pose, biting, and feeding dynamics of Aedes aegypti, Aedes albopictus, Anopheles stephensi, and Anopheles coluzzii. In addition to characterizing behavioral patterns, we discover that the common insect repellent DEET repels Anopheles coluzzii upon contact with their legs. The biteOscope provides a new perspective on mosquito blood feeding, enabling the high-throughput quantitative characterization of this lethal behavior.
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Affiliation(s)
- Felix JH Hol
- Department of Bioengineering, Stanford UniversityStanfordUnited States
- Insect-Virus Interactions Unit, Institut Pasteur, UMR2000, CNRSParisFrance
- Center for research and Interdisciplinarity, U1284 INSERM, Université de ParisParisFrance
| | - Louis Lambrechts
- Insect-Virus Interactions Unit, Institut Pasteur, UMR2000, CNRSParisFrance
| | - Manu Prakash
- Department of Bioengineering, Stanford UniversityStanfordUnited States
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82
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Bala PC, Eisenreich BR, Yoo SBM, Hayden BY, Park HS, Zimmermann J. Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio. Nat Commun 2020; 11:4560. [PMID: 32917899 PMCID: PMC7486906 DOI: 10.1038/s41467-020-18441-5] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 08/10/2020] [Indexed: 11/26/2022] Open
Abstract
The rhesus macaque is an important model species in several branches of science, including neuroscience, psychology, ethology, and medicine. The utility of the macaque model would be greatly enhanced by the ability to precisely measure behavior in freely moving conditions. Existing approaches do not provide sufficient tracking. Here, we describe OpenMonkeyStudio, a deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques in large unconstrained environments. Our system makes use of 62 machine vision cameras that encircle an open 2.45 m × 2.45 m × 2.75 m enclosure. The resulting multiview image streams allow for data augmentation via 3D-reconstruction of annotated images to train a robust view-invariant deep neural network. This view invariance represents an important advance over previous markerless 2D tracking approaches, and allows fully automatic pose inference on unconstrained natural motion. We show that OpenMonkeyStudio can be used to accurately recognize actions and track social interactions.
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Affiliation(s)
- Praneet C Bala
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | | | - Seng Bum Michael Yoo
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Benjamin Y Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA.
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, 55455, USA.
- Center for Neuroengineering, University of Minnesota, Minneapolis, MN, 55455, USA.
| | - Hyun Soo Park
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, 55455, USA
- Center for Neuroengineering, University of Minnesota, Minneapolis, MN, 55455, USA
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83
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84
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Liu C, Zhang B, Zhang L, Yang T, Zhang Z, Gao Z, Zhang W. A neural circuit encoding mating states tunes defensive behavior in Drosophila. Nat Commun 2020; 11:3962. [PMID: 32770059 PMCID: PMC7414864 DOI: 10.1038/s41467-020-17771-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 07/20/2020] [Indexed: 01/07/2023] Open
Abstract
Social context can dampen or amplify the perception of touch, and touch in turn conveys nuanced social information. However, the neural mechanism behind social regulation of mechanosensation is largely elusive. Here we report that fruit flies exhibit a strong defensive response to mechanical stimuli to their wings. In contrast, virgin female flies being courted by a male show a compromised defensive response to the stimuli, but following mating the response is enhanced. This state-dependent switch is mediated by a functional reconfiguration of a neural circuit labelled with the Tmc-L gene in the ventral nerve cord. The circuit receives excitatory inputs from peripheral mechanoreceptors and coordinates the defensive response. While male cues suppress it via a doublesex (dsx) neuronal pathway, mating sensitizes it by stimulating a group of uterine neurons and consequently activating a leucokinin-dependent pathway. Such a modulation is crucial for the balance between defense against body contacts and sexual receptivity. Wing touching induces a defensive response in D. melanogaster. Here, the authors show that female flies change the defensive response during courtship and after mating. This switch is mediated by functional reconfiguration of a neural circuit in the ventral nerve cord.
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Affiliation(s)
- Chenxi Liu
- School of Life Sciences, Tsinghua-Peking Joint Center for Life Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, 100084, Beijing, China
| | - Bei Zhang
- School of Life Sciences, Tsinghua-Peking Joint Center for Life Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, 100084, Beijing, China
| | - Liwei Zhang
- School of Life Sciences, Tsinghua-Peking Joint Center for Life Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, 100084, Beijing, China
| | - Tingting Yang
- School of Life Sciences, Tsinghua-Peking Joint Center for Life Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, 100084, Beijing, China
| | - Zhewei Zhang
- School of Life Sciences, Tsinghua-Peking Joint Center for Life Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, 100084, Beijing, China
| | - Zihua Gao
- School of Life Sciences, Tsinghua-Peking Joint Center for Life Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, 100084, Beijing, China
| | - Wei Zhang
- School of Life Sciences, Tsinghua-Peking Joint Center for Life Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, 100084, Beijing, China.
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85
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Parker PRL, Brown MA, Smear MC, Niell CM. Movement-Related Signals in Sensory Areas: Roles in Natural Behavior. Trends Neurosci 2020; 43:581-595. [PMID: 32580899 PMCID: PMC8000520 DOI: 10.1016/j.tins.2020.05.005] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/02/2020] [Accepted: 05/24/2020] [Indexed: 11/24/2022]
Abstract
Recent studies have demonstrated prominent and widespread movement-related signals in the brain of head-fixed mice, even in primary sensory areas. However, it is still unknown what role these signals play in sensory processing. Why are these sensory areas 'contaminated' by movement signals? During natural behavior, animals actively acquire sensory information as they move through the environment and use this information to guide ongoing actions. In this context, movement-related signals could allow sensory systems to predict self-induced sensory changes and extract additional information about the environment. In this review we summarize recent findings on the presence of movement-related signals in sensory areas and discuss how their study, in the context of natural freely moving behaviors, could advance models of sensory processing.
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Affiliation(s)
- Philip R L Parker
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
| | - Morgan A Brown
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - Matthew C Smear
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA; Department of Psychology, University of Oregon, Eugene, OR 97403, USA
| | - Cristopher M Niell
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA; Department of Biology, University of Oregon, Eugene, OR 97403, USA.
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86
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Groblewski PA, Ollerenshaw DR, Kiggins JT, Garrett ME, Mochizuki C, Casal L, Cross S, Mace K, Swapp J, Manavi S, Williams D, Mihalas S, Olsen SR. Characterization of Learning, Motivation, and Visual Perception in Five Transgenic Mouse Lines Expressing GCaMP in Distinct Cell Populations. Front Behav Neurosci 2020; 14:104. [PMID: 32655383 PMCID: PMC7324787 DOI: 10.3389/fnbeh.2020.00104] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 05/25/2020] [Indexed: 01/01/2023] Open
Abstract
To study the mechanisms of perception and cognition, neural measurements must be made during behavior. A goal of the Allen Brain Observatory is to map the activity of distinct cortical cell classes underlying visual and behavioral processing. Here we describe standardized methodology for training head-fixed mice on a visual change detection task, and we use our paradigm to characterize learning and behavior of five GCaMP6-expressing transgenic lines. We used automated training procedures to facilitate comparisons across mice. Training times varied, but most transgenic mice learned the behavioral task. Motivation levels also varied across mice. To compare mice in similar motivational states we subdivided sessions into over-, under-, and optimally motivated periods. When motivated, the pattern of perceptual decisions were highly correlated across transgenic lines, although overall performance (d-prime) was lower in one line labeling somatostatin inhibitory cells. These results provide important context for using these mice to map neural activity underlying perception and behavior.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Shawn R. Olsen
- Allen Institute for Brain Science, Seattle, WA, United States
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87
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Privat M, Sumbre G. Naturalistic Behavior: The Zebrafish Larva Strikes Back. Curr Biol 2020; 30:R27-R29. [DOI: 10.1016/j.cub.2019.11.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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88
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Jain K, Berman GJ. Opening the black box of social behavior. Nat Neurosci 2019; 22:1947-1948. [PMID: 31768055 DOI: 10.1038/s41593-019-0547-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Kanishk Jain
- Department of Physics, Emory University, Atlanta, Georgia, USA
| | - Gordon J Berman
- Department of Physics, Emory University, Atlanta, Georgia, USA. .,Department of Biology, Emory University, Atlanta, Georgia, USA. .,Initiative in the Theory and Modeling of Living Systems, Emory University, Atlanta, Georgia, USA.
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89
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Datta SR, Anderson DJ, Branson K, Perona P, Leifer A. Computational Neuroethology: A Call to Action. Neuron 2019; 104:11-24. [PMID: 31600508 PMCID: PMC6981239 DOI: 10.1016/j.neuron.2019.09.038] [Citation(s) in RCA: 191] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 09/16/2019] [Accepted: 09/23/2019] [Indexed: 12/11/2022]
Abstract
The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical. Here we discuss considerations in computational neuroethology-the science of quantifying naturalistic behaviors for understanding the brain-and propose strategies to evaluate progress. We point to open questions that require resolution and call upon the broader systems neuroscience community to further develop and leverage measures of naturalistic, unrestrained behavior, which will enable us to more effectively probe the richness and complexity of the brain.
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Affiliation(s)
| | - David J Anderson
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, Pasadena, CA, 91125, USA; Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA 91125, USA
| | - Kristin Branson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Pietro Perona
- Division of Engineering & Applied Sciences 136-93, California Institute of Technology, Pasadena, CA 91125, USA
| | - Andrew Leifer
- Department of Physics, Princeton University, Princeton, NJ 08544, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.
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90
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Musall S, Urai AE, Sussillo D, Churchland AK. Harnessing behavioral diversity to understand neural computations for cognition. Curr Opin Neurobiol 2019; 58:229-238. [PMID: 31670073 PMCID: PMC6931281 DOI: 10.1016/j.conb.2019.09.011] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 08/28/2019] [Accepted: 09/11/2019] [Indexed: 11/28/2022]
Abstract
With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we review emerging conceptual and technological advances that begin to address this challenge, garnering insights from both biological and artificial neural networks. We argue that neural data should be recorded during rich behavioral tasks, to model cognitive processes and estimate latent behavioral variables. Careful quantification of animal movements can also provide a more complete picture of how movements shape neural dynamics and reflect changes in brain state, such as arousal or stress. Artificial neural networks (ANNs) could serve as artificial model organisms to connect neural dynamics and rich behavioral data. ANNs have already begun to reveal how a wide range of different behaviors can be implemented, generating hypotheses about how observed neural activity might drive behavior and explaining diversity in behavioral strategies.
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Affiliation(s)
- Simon Musall
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA
| | - Anne E Urai
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA
| | - David Sussillo
- Google AI, Google, Inc., Mountain View, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Anne K Churchland
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA.
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