Analysis Of Biological Neurons Via Modeling And Rule Mining

Due to experimental constraints, measurement errors and variability, analyzing how the activity of biological neurons depends on cellular parameters can be difficult. Computational modeling of neurons allows for exploration of many parameter combinations and various types of neuronal activity, without requiring a prohibitively large number of “wet” experiments. Databases of model neurons created through parameter exploration can, however, be very extensive. There thus is a need for an automated 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 pseudoassociation rule mining methodology to deal with this task.