Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons
Nasuto, S. J., Krichmar, J. L., Scorcioni, R. and Ascoli, G. A. (2001) Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons. In: International Conference on Complex Systems (ICCS), 21-26 May 2000, Nashua, NH.
Full text not archived in this repository.
We are developing computational tools supporting the detailed analysis of the dependence of neural electrophysiological response on dendritic morphology. We approach this problem by combining simulations of faithful models of neurons (experimental real life morphological data with known models of channel kinetics) with algorithmic extraction of morphological and physiological parameters and statistical analysis. In this paper, we present the novel method for an automatic recognition of spike trains in voltage traces, which eliminates the need for human intervention. This enables classification of waveforms with consistent criteria across all the analyzed traces and so it amounts to reduction of the noise in the data. This method allows for an automatic extraction of relevant physiological parameters necessary for further statistical analysis. In order to illustrate the usefulness of this procedure to analyze voltage traces, we characterized the influence of the somatic current injection level on several electrophysiological parameters in a set of modeled neurons. This application suggests that such an algorithmic processing of physiological data extracts parameters in a suitable form for further investigation of structure-activity relationship in single neurons.
Centaur Editors: Update this record