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Coherence Potentials: Loss-Less, All-or-None Network Events in the Cortex

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      Abstract

      Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential (LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks.

      Author Summary

      Perception and behavior are thought to arise from transient associations among sub-groups of nerve cells in the brain. However, identifying which of the many active neurons are associated at any given time and how poses a challenge. Here we show that when the composite activity of a local group of cortical neurons, measured as a complex waveform in the extracellular field, exceeds a threshold, its activity pattern extending up to hundreds of milliseconds occurs without distortion at other cortical sites via fast synaptic transmission. We call these all-or-none propagated patterns “coherence potentials”, in analogy to action potentials at the single cell level. In contrast to action potentials, which are stereotypical and thus capable only of binary coding, coherence potentials are diverse and complex waveforms that can serve as a high-dimensional parameter for encoding information. The non-linear relationship between local activity and its extent of replicated spread suggests a “tipping point” that bears analogy to the propagation of innovations and economic behavior in social networks, which can spread rapidly once they have garnered a local critical mass.

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      Most cited references 78

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      A mechanism for cognitive dynamics: neuronal communication through neuronal coherence.

       Pascal Fries (2005)
      At any one moment, many neuronal groups in our brain are active. Microelectrode recordings have characterized the activation of single neurons and fMRI has unveiled brain-wide activation patterns. Now it is time to understand how the many active neuronal groups interact with each other and how their communication is flexibly modulated to bring about our cognitive dynamics. I hypothesize that neuronal communication is mechanistically subserved by neuronal coherence. Activated neuronal groups oscillate and thereby undergo rhythmic excitability fluctuations that produce temporal windows for communication. Only coherently oscillating neuronal groups can interact effectively, because their communication windows for input and for output are open at the same times. Thus, a flexible pattern of coherence defines a flexible communication structure, which subserves our cognitive flexibility.
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        The brainweb: phase synchronization and large-scale integration.

        The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of functionally specialized brain regions. Here we review the mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity to enable the emergence of coherent behaviour and cognition. Although the mechanisms involved in large-scale integration are still largely unknown, we argue that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.
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          Community structure in social and biological networks.

           M Girvan,  M Newman (2002)
          A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.
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            Author and article information

            Affiliations
            [1 ]Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, Maryland, United States of America
            [2 ]Department of Neurobiology, Center for Neuroengineering, Duke University, Durham, North Carolina, United States of America
            Author notes

            The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: TCT DP. Performed the experiments: MAL MAN DP. Analyzed the data: TCT DP. Contributed reagents/materials/analysis tools: TCT DP. Wrote the paper: TCT DP. Conceived and designed the analysis: TCT. Performed the recordings in monkey: MAL MAN.

            Contributors
            Role: Academic Editor
            Journal
            PLoS Biol
            plos
            plosbiol
            PLoS Biology
            Public Library of Science (San Francisco, USA )
            1544-9173
            1545-7885
            January 2010
            January 2010
            12 January 2010
            : 8
            : 1
            2795777
            20084093
            08-PLBI-RA-3626R5
            10.1371/journal.pbio.1000278
            (Academic Editor)
            This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
            Counts
            Pages: 18
            Categories
            Research Article
            Neuroscience/Behavioral Neuroscience
            Neuroscience/Cognitive Neuroscience
            Neuroscience/Theoretical Neuroscience

            Life sciences

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