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      Learned Representation of Implied Serial Order in Posterior Parietal Cortex

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          Abstract

          Monkeys can learn the implied ranking of pairs of images drawn from an ordered set, despite never seeing all of the images simultaneously and without explicit spatial or temporal cues. We recorded the activity of posterior parietal cortex (including lateral intraparietal area LIP) neurons while monkeys learned 7-item transitive inference (TI) lists with 2 items presented on each trial. Behavior and neuronal activity were significantly influenced by the ordinal relationship of the stimulus pairs, specifically symbolic distance (the difference in rank) and joint rank (the sum of the ranks). Symbolic distance strongly predicted decision accuracy and learning rate. An effect of joint rank on performance was found nested within the symbolic distance effect. Across the population of neurons, there was significant modulation of firing correlated with the relative ranks of the two stimuli presented on each trial. Neurons exhibited selectivity for stimulus rank during learning, but not before or after. The observed behavior is poorly explained by associative or reward mechanisms, and appears more consistent with a mental workspace model in which implied serial order is mapped within a spatial framework. The neural data suggest that posterior parietal cortex supports serial learning by representing information about the ordinal relationship of the stimuli presented during a given trial.

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          Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.

          This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.
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            Neural correlates of decision variables in parietal cortex.

            Decision theory proposes that humans and animals decide what to do in a given situation by assessing the relative value of each possible response. This assessment can be computed, in part, from the probability that each action will result in a gain and the magnitude of the gain expected. Here we show that the gain (or reward) a monkey can expect to realize from an eye-movement response modulates the activity of neurons in the lateral intraparietal area, an area of primate cortex that is thought to transform visual signals into eye-movement commands. We also show that the activity of these neurons is sensitive to the probability that a particular response will result in a gain. When animals can choose freely between two alternative responses, the choices subjects make and neuronal activation in this area are both correlated with the relative amount of gain that the animal can expect from each response. Our data indicate that a decision-theoretic model may provide a powerful new framework for studying the neural processes that intervene between sensation and action.
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              Interactions between number and space in parietal cortex.

              Since the time of Pythagoras, numerical and spatial representations have been inextricably linked. We suggest that the relationship between the two is deeply rooted in the brain's organization for these capacities. Many behavioural and patient studies have shown that numerical-spatial interactions run far deeper than simply cultural constructions, and, instead, influence behaviour at several levels. By combining two previously independent lines of research, neuroimaging studies of numerical cognition in humans, and physiological studies of spatial cognition in monkeys, we propose that these numerical-spatial interactions arise from common parietal circuits for attention to external space and internal representations of numbers.
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                Author and article information

                Contributors
                vpf3@cumc.columbia.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 June 2020
                10 June 2020
                2020
                : 10
                : 9386
                Affiliations
                [1 ]ISNI 0000 0001 2285 2675, GRID grid.239585.0, Department of Neuroscience, Columbia University Medical Center, ; New York, NY 10032 USA
                [2 ]ISNI 0000000419368729, GRID grid.21729.3f, Zuckerman Mind Brain Behavior Institute, Columbia University, ; New York, NY 10027 USA
                [3 ]ISNI 0000000419368729, GRID grid.21729.3f, Department of Psychology, Columbia University, ; New York, NY 10027 USA
                [4 ]ISNI 0000 0001 2285 2675, GRID grid.239585.0, Department of Neurosurgery, Columbia University Medical Center, ; New York, NY 10032 USA
                [5 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Neurosurgery, University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [6 ]ISNI 0000 0001 2285 2675, GRID grid.239585.0, Department of Psychiatry, Columbia University Medical Center, ; New York, NY 10032 USA
                Article
                65838
                10.1038/s41598-020-65838-9
                7287075
                32523062
                c05e4741-3b05-4066-816f-2fe9a3c7efbc
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 December 2019
                : 8 May 2020
                Categories
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                Custom metadata
                © The Author(s) 2020

                Uncategorized
                neuroscience,cognitive neuroscience,learning and memory,neuronal physiology,oculomotor system,reward,sensorimotor processing,social neuroscience,visual system

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