Model-based source localization of extracellular action potentials

Zoltán Somogyvári, László Zalányi, István Ulbert, Péter Érdi

Research output: Contribution to journalArticle

24 Citations (Scopus)


A new model-based analysis method was set up for revealing information encrypted in extracellular spatial potential patterns of neocortical action potentials. Spikes were measured by extracellular linear multiple microelectrode in vivo cat's primary auditory cortex and were analyzed based on current source density (CSD) distribution models. Validity of the monopole and other point source approximations were tested on the measured potential patterns by numerical fitting. We have found, that point source models could not provide accurate description of the measured patterns. We introduced a new model of the CSD distribution on a spiking cell, called counter-current model (CCM). This new model was shown to provide better description of the spatial current distribution of the cell during the initial negative deflection of the extracellular action potential, from the onset of the spike to the negative peak. The new model was tested on simulated extracellular potentials. We proved numerically, that all the parameters of the model could be determined accurately based on measurements. Thus, fitting of the CCM allowed extraction of these parameters from the measurements. Due to model fitting, CSD could be calculated with much higher accuracy as done with the traditional method because distance dependence of the spatial potential patterns was explicitly taken into consideration in our method. Average CSD distribution of the neocortical action potentials was calculated and spatial decay constant of the dendritic trees was determined by applying our new method.

Original languageEnglish
Pages (from-to)126-137
Number of pages12
JournalJournal of Neuroscience Methods
Issue number2
Publication statusPublished - Sep 30 2005


  • Current source density
  • Dendritic currents
  • Extracellular action potentials

ASJC Scopus subject areas

  • Neuroscience(all)

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