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      CA1-Projecting Subiculum Neurons Facilitate Object-Place Learning


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          Recent anatomical evidence suggests a functionally significant back-projection pathway from the subiculum to CA1. Here we show that the afferent circuitry of CA1-projecting subicular neurons is biased by inputs from CA1 inhibitory neurons as well as visual cortex, but lacks input from entorhinal cortex. Efferents of the CA1-projecting subiculum neurons also target perirhinal cortex, an area strongly implicated in object-place learning. We identify a critical role for CA1-projecting subicular neurons in object-location learning and memory, and show that this projection modulates place-specific activity of CA1 neurons as well as their responses to displaced objects. Together, these experiments reveal a novel pathway by which cortical inputs, particularly those from visual cortex, reach hippocampal output region CA1, and our findings implicate this circuitry in the formation of complex spatial representations and learning of object-place associations.

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          Most cited references63

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          Theta oscillations in the hippocampus.

          Theta oscillations represent the "on-line" state of the hippocampus. The extracellular currents underlying theta waves are generated mainly by the entorhinal input, CA3 (Schaffer) collaterals, and voltage-dependent Ca(2+) currents in pyramidal cell dendrites. The rhythm is believed to be critical for temporal coding/decoding of active neuronal ensembles and the modification of synaptic weights. Nevertheless, numerous critical issues regarding both the generation of theta oscillations and their functional significance remain challenges for future research.
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            DREADDs for Neuroscientists.

            Bryan Roth (2016)
            To understand brain function, it is essential that we discover how cellular signaling specifies normal and pathological brain function. In this regard, chemogenetic technologies represent valuable platforms for manipulating neuronal and non-neuronal signal transduction in a cell-type-specific fashion in freely moving animals. Designer Receptors Exclusively Activated by Designer Drugs (DREADD)-based chemogenetic tools are now commonly used by neuroscientists to identify the circuitry and cellular signals that specify behavior, perceptions, emotions, innate drives, and motor functions in species ranging from flies to nonhuman primates. Here I provide a primer on DREADDs highlighting key technical and conceptual considerations and identify challenges for chemogenetics going forward.
              • Record: found
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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.

                Author and article information

                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                24 August 2019
                23 September 2019
                November 2019
                23 March 2020
                : 22
                : 11
                : 1857-1870
                [1. ]Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697-1275
                [2. ]Department of Mathematics and Department of Developmental & Cell Biology, University of California, Irvine, CA 92697-3875
                [3. ]Departments of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY 10027
                [4. ]Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
                [5. ]West Los Angeles VA Medical Center, Los Angeles, CA 90073
                [6. ]Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, CA 92697-4560
                [7. ]Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093
                [8. ]Department of Biomedical Engineering, University of California, Irvine, CA 92697-2715
                [9. ]Department of Microbiology and Molecular Genetics, University of California, Irvine, CA 92697-4025
                [10. ]Department of Computer Science, University of California, Irvine, CA 92697-3435
                Author notes

                Author Contributions: X.X., Y.S. and D.A.N. designed experiments. Y.S. and X.L. performed viral tracing, miniscope imaging and mouse behavioral experiments. X.Q. and L.J. performed electrophysiological recordings. S.J., K.G.J., and Q.N. performed computational analysis. S.J., Y.S. and L.C. developed codes and analyzed imaging data with the help from P.Z., D.A.N., and Q.N. P.G. contributed to the miniscope imaging application. X.X., D.A.N., Y.S., T.C.H. and S.J. analyzed and interpreted the data, wrote the manuscript and prepared the figures. X.X. oversaw the project.


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