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      Initial-state-dependent, robust, transient neural dynamics encode conscious visual perception

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      PLoS Computational Biology
      Public Library of Science

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          Abstract

          Recent research has identified late-latency, long-lasting neural activity as a robust correlate of conscious perception. Yet, the dynamical nature of this activity is poorly understood, and the mechanisms governing its presence or absence and the associated conscious perception remain elusive. We applied dynamic-pattern analysis to whole-brain slow (< 5 Hz) cortical dynamics recorded by magnetoencephalography (MEG) in human subjects performing a threshold-level visual perception task. Up to 1 second before stimulus onset, brain activity pattern across widespread cortices significantly predicted whether a threshold-level visual stimulus was later consciously perceived. This initial state of brain activity interacts nonlinearly with stimulus input to shape the evolving cortical activity trajectory, with seen and unseen trials following well separated trajectories. We observed that cortical activity trajectories during conscious perception are fast evolving and robust to small variations in the initial state. In addition, spontaneous brain activity pattern prior to stimulus onset also influences unconscious perceptual making in unseen trials. Together, these results suggest that brain dynamics underlying conscious visual perception belongs to the class of initial-state-dependent, robust, transient neural dynamics.

          Author summary

          What brain mechanisms underlie conscious perception? A commonly adopted paradigm for studying this question is to present human subjects with threshold-level stimuli. When shown repeatedly, the same stimulus is sometimes consciously perceived, sometimes not. Using magnetoencephalography, we shed light on the neural mechanisms governing whether the stimulus is consciously perceived in a given trial. We observed that depending on the initial brain state defined by widespread activity pattern in the slow cortical potential (<5 Hz) range, a physically identical, brief (30–60 ms) stimulus input triggers distinct sequences of activity pattern evolution over time that correspond to either consciously perceiving the stimulus or not. Such activity pattern evolution forms a “trajectory” in the state space and affords significant single-trial decoding of perceptual outcome from 1 sec before to 3 sec after stimulus onset. While previous theories on conscious perception have emphasized sustained, high-level activity, we found that brain dynamics underlying conscious perception exhibit fast-changing activity patterns. These results significantly further our understanding on the neural mechanisms governing conscious access of a stimulus and the dynamical nature of distributed neural activity underlying conscious perception.

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          Most cited references47

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          LIBSVM: A library for support vector machines

          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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            Real-time computing without stable states: a new framework for neural computation based on perturbations.

            A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.
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              Confidence Intervals from Normalized Data: A correction to Cousineau (2005)

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: 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
                27 November 2017
                November 2017
                : 13
                : 11
                : e1005806
                Affiliations
                [1 ] National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, United States of America
                [2 ] Neuroscience Institute, New York University Langone Medical Center, New York, NY, United States of America
                [3 ] Departments of Neurology, Neuroscience and Physiology, and Radiology, New York University Langone Medical Center, New York, NY, United States of America
                University of Birmingham School of Psychology, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                [¤]

                Current address: Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America

                Author information
                http://orcid.org/0000-0001-6999-4021
                http://orcid.org/0000-0003-1549-1351
                Article
                PCOMPBIOL-D-17-00571
                10.1371/journal.pcbi.1005806
                5720802
                29176808
                c173434d-2e66-4f90-9ed1-590f6e7e50c7

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 6 April 2017
                : 1 October 2017
                Page count
                Figures: 10, Tables: 0, Pages: 29
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100007027, Leon Levy Foundation;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100001207, Esther A. and Joseph Klingenstein Fund;
                Award Recipient :
                This research was supported by the Intramural Research Program of the National Institutes of Health/National Institute of Neurological Disorders and Stroke ( https://www.ninds.nih.gov), New York University Langone Medical Center ( http://nyulangone.org), Leon Levy Foundation ( http://leonlevyfoundation.org) and Klingenstein-Simons Fellowship ( http://www.klingfund.org). All sources of funding were awarded to BJH. 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
                Neuroscience
                Brain Mapping
                Magnetoencephalography
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Magnetoencephalography
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Magnetoencephalography
                Biology and Life Sciences
                Neuroscience
                Sensory Perception
                Vision
                Biology and Life Sciences
                Psychology
                Sensory Perception
                Vision
                Social Sciences
                Psychology
                Sensory Perception
                Vision
                Physical Sciences
                Mathematics
                Discrete Mathematics
                Combinatorics
                Permutation
                Biology and Life Sciences
                Neuroscience
                Sensory Perception
                Biology and Life Sciences
                Psychology
                Sensory Perception
                Social Sciences
                Psychology
                Sensory Perception
                Computer and Information Sciences
                Systems Science
                Dynamical Systems
                Physical Sciences
                Mathematics
                Systems Science
                Dynamical Systems
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Neuroscience
                Consciousness
                Access to Consciousness
                Biology and Life Sciences
                Neuroscience
                Cognitive Neuroscience
                Consciousness
                Access to Consciousness
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Neuroscience
                Consciousness
                Theories of Consciousness
                Biology and Life Sciences
                Neuroscience
                Cognitive Neuroscience
                Consciousness
                Theories of Consciousness
                Custom metadata
                vor-update-to-uncorrected-proof
                2017-12-07
                Data are available from NYU Langone Medical Center Data Access, He lab website: https://med.nyu.edu/helab/publications.

                Quantitative & Systems biology
                Quantitative & Systems biology

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