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      The emergence and influence of internal states

      , , ,
      Neuron
      Elsevier BV

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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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            A theory of human motivation.

            A. MASLOW (1943)
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              The human brain is intrinsically organized into dynamic, anticorrelated functional networks.

              During performance of attention-demanding cognitive tasks, certain regions of the brain routinely increase activity, whereas others routinely decrease activity. In this study, we investigate the extent to which this task-related dichotomy is represented intrinsically in the resting human brain through examination of spontaneous fluctuations in the functional MRI blood oxygen level-dependent signal. We identify two diametrically opposed, widely distributed brain networks on the basis of both spontaneous correlations within each network and anticorrelations between networks. One network consists of regions routinely exhibiting task-related activations and the other of regions routinely exhibiting task-related deactivations. This intrinsic organization, featuring the presence of anticorrelated networks in the absence of overt task performance, provides a critical context in which to understand brain function. We suggest that both task-driven neuronal responses and behavior are reflections of this dynamic, ongoing, functional organization of the brain.
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                Author and article information

                Journal
                Neuron
                Neuron
                Elsevier BV
                08966273
                May 2022
                May 2022
                Article
                10.1016/j.neuron.2022.04.030
                35643077
                f0b1df6e-d32a-479f-9d1c-d252f5163b81
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

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