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      Cortical pattern generation during dexterous movement is input-driven

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Motor cortex controls skilled arm movement by sending temporal patterns of activity to lower motor centers 1 . Local cortical dynamics are thought to shape these patterns throughout movement execution 24 . External inputs have been implicated in setting the initial state of motor cortex 5, 6 , but they may also have a pattern-generating role. Here, we dissect the contribution of local dynamics and inputs to cortical pattern generation during a prehension task in mice. Perturbing cortex to an aberrant state prevented movement initiation, but after the perturbation was released, cortex either bypassed the normal initial state and immediately generated the pattern that controls reaching, or it failed to generate this pattern. The difference in these two outcomes was likely due to external inputs. We directly investigated the role of inputs by inactivating thalamus; this perturbed cortical activity and disrupted limb kinematics at any stage of the movement. Activation of thalamocortical axon terminals at different frequencies disrupted cortical activity and arm movement in a graded manner. Simultaneous recordings revealed that both thalamic activity and the current state of cortex predicted changes in cortical activity. Thus, the pattern generator for dexterous arm movement is distributed across multiple, strongly-interacting brain regions.

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          Most cited references53

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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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            Fully integrated silicon probes for high-density recording of neural activity

            Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal–oxide–semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.
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              Neural population dynamics during reaching

              Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from an analogous approach to primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well that analogy holds. Single-neuron responses in motor cortex appear strikingly complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. We found that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behavior. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate unexpected yet surprisingly simple structure in the population response. That underlying structure explains many of the confusing features of individual-neuron responses.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                31 October 2019
                25 December 2019
                January 2020
                25 June 2020
                : 577
                : 7790
                : 386-391
                Affiliations
                Janelia Research Campus, Howard Hughes Medical Institute
                Author notes
                [++]

                Columbia University

                [*]

                These authors contributed equally to this work.

                Author contributions

                B.S., J.G., J.C., and A.H. designed the experiments. B.S. and J.G. performed electrophysiological recordings in motor cortex. J.G. performed recordings in cortex with thalamic inactivation. J.C. performed simultaneous recordings in cortex and thalamus and recordings in cortex during stimulation of thalamic terminals. J.G. performed behavioral experiments. B.S. analyzed electrophysiology and behavior data and generated the figures. J.G. and W.G. analyzed behavior data. M.M. developed and performed the neural decoding analyses. B.S., M.M., and A.H. interpreted the results. N.V. and K.B. developed preliminary analyses for decoding of behavioral waypoints. M.K. and K.B. developed computer vision algorithms and software. B.S., A.H., M.M., B.M., and K.B. wrote the paper with input from all authors. A.H. supervised the project.

                [** ]Correspondence: hantmana@ 123456janelia.hhmi.org
                Article
                HHMIMS1541613
                10.1038/s41586-019-1869-9
                6962553
                31875851
                32e4652a-930a-4d4b-94d9-a874d758fdd3

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