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      Differential receptive field organizations give rise to nearly identical neural correlations across three parallel sensory maps in weakly electric fish

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      PLoS Computational Biology
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          Abstract

          Understanding how neural populations encode sensory information thereby leading to perception and behavior (i.e., the neural code) remains an important problem in neuroscience. When investigating the neural code, one must take into account the fact that neural activities are not independent but are actually correlated with one another. Such correlations are seen ubiquitously and have a strong impact on neural coding. Here we investigated how differences in the antagonistic center-surround receptive field (RF) organization across three parallel sensory maps influence correlations between the activities of electrosensory pyramidal neurons. Using a model based on known anatomical differences in receptive field center size and overlap, we initially predicted large differences in correlated activity across the maps. However, in vivo electrophysiological recordings showed that, contrary to modeling predictions, electrosensory pyramidal neurons across all three segments displayed nearly identical correlations. To explain this surprising result, we incorporated the effects of RF surround in our model. By systematically varying both the RF surround gain and size relative to that of the RF center, we found that multiple RF structures gave rise to similar levels of correlation. In particular, incorporating known physiological differences in RF structure between the three maps in our model gave rise to similar levels of correlation. Our results show that RF center overlap alone does not determine correlations which has important implications for understanding how RF structure influences correlated neural activity.

          Author summary

          Growing evidence across nervous systems and species shows that the activities of neighboring neurons are not independent but are correlated with one another, which has important implications for neural coding. Such correlations are generally thought to be due to shared input. However, how this shared input is integrated by neurons in order to give rise to correlated activity is not well understood in general. Here we investigated how receptive field structure determines correlations between the activities of electrosensory pyramidal neurons in weakly electric fish. To do so, we used a combination of mathematical modeling of the known antagonistic center-surround RF structure as well as in vivo electrophysiological recordings. Our results show that the amount of receptive field center overlap alone is not sufficient to explain experimentally observed neural correlations in general. This is because our experimental data shows that pyramidal neurons with very different amounts of receptive field center overlap display almost identical correlations between their activities. Further, our modeling shows that both receptive field center and surround play important roles in determining correlated activity, such that very different combinations of relative RF surround strength and size can generate nearly identical correlations between neural activities. We discuss the implications of our results for sensory processing.

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          Neural correlations, population coding and computation.

          How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.
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            Attention improves performance primarily by reducing interneuronal correlations

            Visual attention can dramatically improve behavioural performance by allowing observers to focus on the important information in a complex scene. Attention also typically increases the firing rates of cortical sensory neurons. Rate increases improve the signal-to-noise ratio of individual neurons, and this improvement has been assumed to underlie attention-related improvements in behaviour. We recorded dozens of neurons simultaneously in visual area V4 and found that changes in single neurons accounted for only a small fraction of the improvement in the sensitivity of the population. Instead, over 80% of the attentional improvement in the population signal was caused by decreases in the correlations between the trial-to-trial fluctuations in the responses of pairs of neurons. These results suggest that the representation of sensory information in populations of neurons and the way attention affects the sensitivity of the population may only be understood by considering the interactions between neurons.
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              Decorrelated neuronal firing in cortical microcircuits.

              Correlated trial-to-trial variability in the activity of cortical neurons is thought to reflect the functional connectivity of the circuit. Many cortical areas are organized into functional columns, in which neurons are believed to be densely connected and to share common input. Numerous studies report a high degree of correlated variability between nearby cells. We developed chronically implanted multitetrode arrays offering unprecedented recording quality to reexamine this question in the primary visual cortex of awake macaques. We found that even nearby neurons with similar orientation tuning show virtually no correlated variability. Our findings suggest a refinement of current models of cortical microcircuit architecture and function: Either adjacent neurons share only a few percent of their inputs or, alternatively, their activity is actively decorrelated.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                1 September 2017
                September 2017
                : 13
                : 9
                : e1005716
                Affiliations
                [001]Department of Physiology, McGill University, McIntyre Medical Building, Montreal, Québec, Canada
                University of California at Berkeley, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-5149-603X
                http://orcid.org/0000-0002-3032-452X
                Article
                PCOMPBIOL-D-17-00885
                10.1371/journal.pcbi.1005716
                5599069
                28863136
                03415159-3731-47ef-9ecd-01e6fa77e158
                © 2017 Hofmann, Chacron

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 June 2017
                : 9 August 2017
                Page count
                Figures: 7, Tables: 0, Pages: 28
                Funding
                Funded by: Deutsche Forschungsgemeinschaft
                Award ID: HO 5912/1-1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award Recipient :
                This research was supported by the Deutsche Forschungsgemeinschaft DFG (VH; HO 5912/1-1) and the Canadian Institutes of Health Research and the Canada Research Chairs (MJC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Neurons
                Biology and Life Sciences
                Neuroscience
                Cellular Neuroscience
                Neurons
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Neurons
                Ganglion Cells
                Pyramidal Cells
                Biology and Life Sciences
                Neuroscience
                Cellular Neuroscience
                Neurons
                Ganglion Cells
                Pyramidal Cells
                Biology and Life Sciences
                Physiology
                Electrophysiology
                Membrane Potential
                Action Potentials
                Medicine and Health Sciences
                Physiology
                Electrophysiology
                Membrane Potential
                Action Potentials
                Biology and Life Sciences
                Physiology
                Electrophysiology
                Neurophysiology
                Action Potentials
                Medicine and Health Sciences
                Physiology
                Electrophysiology
                Neurophysiology
                Action Potentials
                Biology and Life Sciences
                Neuroscience
                Neurophysiology
                Action Potentials
                Biology and Life Sciences
                Computational Biology
                Computational Neuroscience
                Coding Mechanisms
                Biology and Life Sciences
                Neuroscience
                Computational Neuroscience
                Coding Mechanisms
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Neurons
                Afferent Neurons
                Biology and Life Sciences
                Neuroscience
                Cellular Neuroscience
                Neurons
                Afferent Neurons
                Research and Analysis Methods
                Bioassays and Physiological Analysis
                Electrophysiological Techniques
                Membrane Electrophysiology
                Electrode Recording
                Biology and Life Sciences
                Neuroscience
                Sensory Perception
                Vision
                Biology and Life Sciences
                Psychology
                Sensory Perception
                Vision
                Social Sciences
                Psychology
                Sensory Perception
                Vision
                Biology and Life Sciences
                Zoology
                Fish Biology
                Fish Physiology
                Biology and Life Sciences
                Zoology
                Animal Physiology
                Vertebrate Physiology
                Fish Physiology
                Custom metadata
                vor-update-to-uncorrected-proof
                2017-09-14
                All data are available from the figshare database ( https://figshare.com/articles/Files_for_baseline_correlations/5256751).

                Quantitative & Systems biology
                Quantitative & Systems biology

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