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      The Spatiotemporal Organization of the Striatum Encodes Action Space

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          Summary

          Activity in striatal direct- and indirect-pathway spiny projection neurons (SPNs) is critical for proper movement. However, little is known about the spatiotemporal organization of this activity. We investigated the spatiotemporal organization of SPN ensemble activity in mice during self-paced, natural movements using microendoscopic imaging. Activity in both pathways showed predominantly local but also some long-range correlations. Using a novel approach to cluster and quantify behaviors based on continuous accelerometer and video data, we found that SPN ensembles active during specific actions were spatially closer and more correlated overall. Furthermore, similarity between different actions corresponded to the similarity between SPN ensemble patterns, irrespective of movement speed. Consistently, the accuracy of decoding behavior from SPN ensemble patterns was directly related to the dissimilarity between behavioral clusters. These results identify a predominantly local, but not spatially compact, organization of direct- and indirect-pathway SPN activity that maps action space independently of movement speed.

          Highlights

          • Direct- and indirect-pathway SPNs show locally biased spatiotemporal organization

          • SPNs active during a particular behavior are more correlated and spatially closer

          • SPN ensembles encode action identity independently of movement speed

          • Distance between behaviors corresponds to distance between SPN ensemble patterns

          Abstract

          Klaus, Martins et al. image neural activity from direct- and indirect-pathway neurons of dorsal striatum during self-paced, natural behaviors. They find that striatal neurons form ensembles that are spatiotemporally organized and map action space independently of movement speed.

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          Most cited references25

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          Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.

          We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.
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            Mapping Sub-Second Structure in Mouse Behavior.

            Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that 3D mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior.
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              Broadly tuned response properties of diverse inhibitory neuron subtypes in mouse visual cortex.

              Different subtypes of GABAergic neurons in sensory cortex exhibit diverse morphology, histochemical markers, and patterns of connectivity. These subtypes likely play distinct roles in cortical function, but their in vivo response properties remain unclear. We used in vivo calcium imaging, combined with immunohistochemical and genetic labels, to record visual responses in excitatory neurons and up to three distinct subtypes of GABAergic neurons (immunoreactive for parvalbumin, somatostatin, or vasoactive intestinal peptide) in layer 2/3 of mouse visual cortex. Excitatory neurons had sharp response selectivity for stimulus orientation and spatial frequency, while all GABAergic subtypes had broader selectivity. Further, bias in the responses of GABAergic neurons toward particular orientations or spatial frequencies tended to reflect net biases of the surrounding neurons. These results suggest that the sensory responses of layer 2/3 GABAergic neurons reflect the pooled activity of the surrounding population--a principle that may generalize across species and sensory modalities. 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Neuron
                Neuron
                Neuron
                Cell Press
                0896-6273
                1097-4199
                30 August 2017
                30 August 2017
                : 95
                : 5
                : 1171-1180.e7
                Affiliations
                [1 ]Champalimaud Neuroscience Program, Champalimaud Foundation, Lisbon 1400-038, Portugal
                [2 ]Center for the Neural Basis of Cognition and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
                [3 ]Department of Statistics, Columbia University, New York, NY 10027, USA
                [4 ]Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
                Author notes
                [∗∗ ]Corresponding author rc3031@ 123456columbia.edu
                [5]

                These authors contributed equally

                [6]

                Lead Contact

                Article
                S0896-6273(17)30730-4
                10.1016/j.neuron.2017.08.015
                5584673
                28858619
                a30bcbcb-3266-4bf3-bce3-0203cc7b50b6
                © 2017 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 26 March 2017
                : 12 July 2017
                : 9 August 2017
                Categories
                Article

                Neurosciences
                basal ganglia,direct pathway,indirect pathway,caudate,putamen,calcium imaging,neural correlation,population activity,action space,behavior decoding

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