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      Spatiotemporal dynamics of information encoding revealed in orbitofrontal high-gamma

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      1 , 2 , , 1 , 3
      Nature Communications
      Nature Publishing Group UK

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          Abstract

          High-gamma signals mirror the tuning and temporal profiles of neurons near a recording electrode in sensory and motor areas. These frequencies appear to aggregate local neuronal activity, but it is unclear how this relationship affects information encoding in high-gamma activity (HGA) in cortical areas where neurons are heterogeneous in selectivity and temporal responses, and are not functionally clustered. Here we report that populations of neurons and HGA recorded from the orbitofrontal cortex (OFC) encode similar information, although there is little correspondence between signals recorded by the same electrode. HGA appears to aggregate heterogeneous neuron activity, such that the spiking of a single cell corresponds to only small increases in HGA. Interestingly, large-scale spatiotemporal dynamics are revealed in HGA, but less apparent in the population of single neurons. Overall, HGA is closely related to neuron activity in OFC, and provides a unique means of studying large-scale spatiotemporal dynamics of information processing.

          Abstract

          High gamma activity (HGA) and local neurons encode similar information, but it’s unclear if this is true when neurons are heterogeneous, as in the orbitofrontal cortex (OFC). Here, Rich & Wallis show that HGA in OFC is closely related to neuron firing, but reveals clearer spatiotemporal dynamics.

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          Most cited references44

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          The importance of mixed selectivity in complex cognitive tasks.

          Single-neuron activity in the prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to the PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input-output functions implementable by readout neurons. This advantage originates from the highly diverse nonlinear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings recommend a shift of focus for future studies from neurons that have easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons.
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            Neurons in the orbitofrontal cortex encode economic value.

            Economic choice is the behaviour observed when individuals select one among many available options. There is no intrinsically 'correct' answer: economic choice depends on subjective preferences. This behaviour is traditionally the object of economic analysis and is also of primary interest in psychology. However, the underlying mental processes and neuronal mechanisms are not well understood. Theories of human and animal choice have a cornerstone in the concept of 'value'. Consider, for example, a monkey offered one raisin versus one piece of apple: behavioural evidence suggests that the animal chooses by assigning values to the two options. But where and how values are represented in the brain is unclear. Here we show that, during economic choice, neurons in the orbitofrontal cortex (OFC) encode the value of offered and chosen goods. Notably, OFC neurons encode value independently of visuospatial factors and motor responses. If a monkey chooses between A and B, neurons in the OFC encode the value of the two goods independently of whether A is presented on the right and B on the left, or vice versa. This trait distinguishes the OFC from other brain areas in which value modulates activity related to sensory or motor processes. Our results have broad implications for possible psychological models, suggesting that economic choice is essentially choice between goods rather than choice between actions. In this framework, neurons in the OFC seem to be a good candidate network for value assignment underlying economic choice.
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              Chronux: a platform for analyzing neural signals.

              Chronux is an open-source software package developed for the analysis of neural data. The current version of Chronux includes software for signal processing of neural time-series data including several specialized mini-packages for spike-sorting, local regression, audio segmentation, and other data-analysis tasks typically encountered by a neuroscientist. Chronux is freely available along with user tutorials, sample data, and extensive documentation from http://chronux.org/. Copyright 2010 Elsevier B.V. All rights reserved.
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                Author and article information

                Contributors
                erin.rich@mssm.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                26 October 2017
                26 October 2017
                2017
                : 8
                : 1139
                Affiliations
                [1 ]ISNI 0000 0001 2181 7878, GRID grid.47840.3f, Helen Wills Neuroscience Institute, , University of California at Berkeley, ; Berkeley, CA 94720 USA
                [2 ]ISNI 0000 0001 0670 2351, GRID grid.59734.3c, Friedman Brain Institute and Department of Neuroscience, , Icahn School of Medicine at Mount Sinai, ; New York, NY 10029 USA
                [3 ]ISNI 0000 0001 2181 7878, GRID grid.47840.3f, Department of Psychology, , University of California at Berkeley, ; Berkeley, CA 94720 USA
                Article
                1253
                10.1038/s41467-017-01253-5
                5658402
                29074960
                82d7ab38-781f-43ee-ab8d-16a4248f154d
                © The Author(s) 2017

                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
                : 26 June 2017
                : 31 August 2017
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