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      Sniff-synchronized, gradient-guided olfactory search by freely moving mice

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          Abstract

          For many organisms, searching for relevant targets such as food or mates entails active, strategic sampling of the environment. Finding odorous targets may be the most ancient search problem that motile organisms evolved to solve. While chemosensory navigation has been well characterized in microorganisms and invertebrates, spatial olfaction in vertebrates is poorly understood. We have established an olfactory search assay in which freely moving mice navigate noisy concentration gradients of airborne odor. Mice solve this task using concentration gradient cues and do not require stereo olfaction for performance. During task performance, respiration and nose movement are synchronized with tens of milliseconds precision. This synchrony is present during trials and largely absent during inter-trial intervals, suggesting that sniff-synchronized nose movement is a strategic behavioral state rather than simply a constant accompaniment to fast breathing. To reveal the spatiotemporal structure of these active sensing movements, we used machine learning methods to parse motion trajectories into elementary movement motifs. Motifs fall into two clusters, which correspond to investigation and approach states. Investigation motifs lock precisely to sniffing, such that the individual motifs preferentially occur at specific phases of the sniff cycle. The allocentric structure of investigation and approach indicates an advantage to sampling both sides of the sharpest part of the odor gradient, consistent with a serial-sniff strategy for gradient sensing. This work clarifies sensorimotor strategies for mouse olfactory search and guides ongoing work into the underlying neural mechanisms.

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          DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

          Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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            Spontaneous behaviors drive multidimensional, brainwide activity

            Neuronal populations in sensory cortex produce variable responses to sensory stimuli and exhibit intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Recording more than 10,000 neurons in mouse visual cortex, we observed that spontaneous activity reliably encoded a high-dimensional latent state, which was partially related to the mouse’s ongoing behavior and was represented not just in visual cortex but also across the forebrain. Sensory inputs did not interrupt this ongoing signal but added onto it a representation of external stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.
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              Using DeepLabCut for 3D markerless pose estimation across species and behaviors

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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                04 May 2021
                2021
                : 10
                : e58523
                Affiliations
                [1 ]Department of Biology and Institute of Neuroscience, University of Oregon EugeneUnited States
                [2 ]Department of Psychology and Institute of Neuroscience, University of Oregon EugeneUnited States
                [3 ]Computational & Biological Learning Lab, University of Cambridge CambridgeUnited Kingdom
                Tata Institute of Fundamental Research India
                Harvard University United States
                Tata Institute of Fundamental Research India
                Author notes
                [†]

                These authors also contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-2050-4869
                https://orcid.org/0000-0001-8096-5766
                http://orcid.org/0000-0002-9652-8103
                https://orcid.org/0000-0002-5942-0697
                https://orcid.org/0000-0003-4689-388X
                Article
                58523
                10.7554/eLife.58523
                8169121
                33942713
                aeff788c-3d89-48d1-943b-5175701fec15
                © 2021, Findley et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 03 May 2020
                : 22 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100001391, Whitehall Foundation;
                Award ID: 2015-12-201
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R56DC015584
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: R21NS104935
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: R34NS116731
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000055, National Institute on Deafness and Other Communication Disorders;
                Award ID: F31DC016799
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: F32MH118724
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100011348, University of Oregon;
                Award ID: Start up funds
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                To track odors borne on turbulent airflow, mice strategically follow their nose, with sampling movements and sensory computations reminiscent of those demonstrated in nematodes and insects.

                Life sciences
                olfaction,active sensing,sniff,neuroethology,search,navigation,mouse
                Life sciences
                olfaction, active sensing, sniff, neuroethology, search, navigation, mouse

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