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      Neural encoding of actual and imagined touch within human posterior parietal cortex

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          Abstract

          In the human posterior parietal cortex (PPC), single units encode high-dimensional information with partially mixed representations that enable small populations of neurons to encode many variables relevant to movement planning, execution, cognition, and perception. Here, we test whether a PPC neuronal population previously demonstrated to encode visual and motor information is similarly engaged in the somatosensory domain. We recorded neurons within the PPC of a human clinical trial participant during actual touch presentation and during a tactile imagery task. Neurons encoded actual touch at short latency with bilateral receptive fields, organized by body part, and covered all tested regions. The tactile imagery task evoked body part-specific responses that shared a neural substrate with actual touch. Our results are the first neuron-level evidence of touch encoding in human PPC and its cognitive engagement during a tactile imagery task, which may reflect semantic processing, attention, sensory anticipation, or imagined touch.

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            Dimensionality reduction for large-scale neural recordings.

            Most sensory, cognitive and motor functions depend on the interactions of many neurons. In recent years, there has been rapid development and increasing use of technologies for recording from large numbers of neurons, either sequentially or simultaneously. A key question is what scientific insight can be gained by studying a population of recorded neurons beyond studying each neuron individually. Here, we examine three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets. Many recent studies have adopted dimensionality reduction to analyze these populations and to find features that are not apparent at the level of individual neurons. We describe the dimensionality reduction methods commonly applied to population activity and offer practical advice about selecting methods and interpreting their outputs. This review is intended for experimental and computational researchers who seek to understand the role dimensionality reduction has had and can have in systems neuroscience, and who seek to apply these methods to their own data.
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              The neural and computational bases of semantic cognition.

              Semantic cognition refers to our ability to use, manipulate and generalize knowledge that is acquired over the lifespan to support innumerable verbal and non-verbal behaviours. This Review summarizes key findings and issues arising from a decade of research into the neurocognitive and neurocomputational underpinnings of this ability, leading to a new framework that we term controlled semantic cognition (CSC). CSC offers solutions to long-standing queries in philosophy and cognitive science, and yields a convergent framework for understanding the neural and computational bases of healthy semantic cognition and its dysfunction in brain disorders.
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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
                01 March 2021
                2021
                : 10
                : e61646
                Affiliations
                [1 ]Department of Biology and Biological Engineering, California Institute of Technology PasadenaUnited States
                [2 ]Tianqiao and Chrissy Chen Brain-Machine Interface Center, Chen Institute for Neuroscience, California Institute of Technology PasadenaUnited States
                [3 ]Geffen School of Medicine, University of California, Los Angeles Los AngelesUnited States
                University College London United Kingdom
                University College London United Kingdom
                University College London United Kingdom
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-3570-162X
                https://orcid.org/0000-0002-0101-2455
                http://orcid.org/0000-0002-0426-3241
                https://orcid.org/0000-0002-7947-0472
                Article
                61646
                10.7554/eLife.61646
                7924956
                33647233
                75c6463d-8149-47b0-863d-536bf98f815b
                © 2021, Chivukula 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
                : 31 July 2020
                : 08 February 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000053, National Eye Institute;
                Award ID: R01EY015545
                Award Recipient :
                Funded by: Conte Center;
                Award ID: P50MH094258
                Award Recipient :
                Funded by: T&C Chen Brain-Machine Interface Center at Caltech;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100009822, Boswell Foundation;
                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
                Single neurons in human posterior parietal cortex encode actual and imagined touch within a shared neural substrate.

                Life sciences
                touch,human,imagery,single neuron
                Life sciences
                touch, human, imagery, single neuron

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