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Commentary by Zoltán Somogyvári and Péter Érdi

Forward and Backward Modeling: From Single Cells to Neural Population and Back

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Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 39))

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Abstract

Some aspects of forward and backward neural modeling are discussed, showing, that the neural mass models may provide a “golden midway” between the detailed conductance based neuron models and the oversimplified models, dealing with the input–output transformations only. Our analysis combines historical perspectives and recent developments concerning neural mass models as a third option for modeling large neural populations and inclusion of detailed anatomical data into them. The current source density analysis and the geometrical assumption behind the different methods, as an inverse modeling tool for determination of the sources of the local field potential is discussed, with special attention to the recent results about source localization on single neurons. These new applications may pave the way to the emergence of a new field of micro-electric imaging .

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Acknowledgments

ZS was supported by grant OTKA K 113147. PE thanks to the Henry Luce Foundation to let him to be a Henry R Luce Professor.

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Authors and Affiliations

  1. Department of Theory, Wigner Research Center for Physics of the Hungarian Academy of Science, Konkoly-Thege Miklós út 29-33, 1121, Budapest, Hungary

    Zoltán Somogyvári & Péter Érdi

  2. Department of Epilepsy and General Neurology, National Institute of Clinical Neurosciences, Budapest, Hungary

    Zoltán Somogyvári

  3. Center for Complex System Studies, Kalamazoo College, 1200 Academy Street, Kalamazoo, Mi, 49006, USA

    Péter Érdi

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  1. Zoltán Somogyvári
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Somogyvári, Z., Érdi, P. (2016). Commentary by Zoltán Somogyvári and Péter Érdi. In: Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Studies in Systems, Decision and Control, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-24406-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-24406-8_13

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