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ANALYSIS OF BIOLOGICAL NEURONS VIA MODELING AND RULE MINING
BibTeX
@MISC{Smolinski_analysisof,
author = {Tomasz G. Smolinski and Pascale Rabbah and Cristina Soto-treviño and Farzan Nadim and Astrid A. Prinz and Tomasz G. Smolinski and Pascale Rabbah and Cristina Soto-treviño and Farzan Nadim and Astrid A. Prinz},
title = {ANALYSIS OF BIOLOGICAL NEURONS VIA MODELING AND RULE MINING},
year = {}
}
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Abstract
Due to experimental constraints, measurement errors and variability, an-alyzing how the activity of biological neurons depends on cellular pa-rameters can be difficult. Computational modeling of neurons allows for exploration of many parameter combinations and various types of neu-ronal activity, without requiring a prohibitively large number of “wet” experiments. Databases of model neurons created through parameter ex-ploration can, however, be very extensive. There thus is a need for an au-tomated analysis of high-dimensional parameter spaces to explain how neuronal parameters influence the output activity of the modeled cells. In this article, we propose an evolutionary algorithms-based pseudo-association rule mining methodology to deal with this task.
