Forward and backward modeling: From single cells to neural population and back

Zoltán Somogyvári, Péter Érdi

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationStudies in Systems, Decision and Control
PublisherSpringer International Publishing
Pages135-146
Number of pages12
DOIs
Publication statusPublished - Jan 1 2016

Publication series

NameStudies in Systems, Decision and Control
Volume39
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

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Keywords

  • Current source density
  • Inverse solution
  • Local field potential
  • Mesoscopic neurodynamic
  • Neural networks
  • Neural population models
  • Single cell modeling

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Systems Engineering
  • Automotive Engineering
  • Social Sciences (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)
  • Control and Optimization
  • Decision Sciences (miscellaneous)

Cite this

Somogyvári, Z., & Érdi, P. (2016). Forward and backward modeling: From single cells to neural population and back. In Studies in Systems, Decision and Control (pp. 135-146). (Studies in Systems, Decision and Control; Vol. 39). Springer International Publishing. https://doi.org/10.1007/978-3-319-24406-8_13