Computational intelligence in modeling of biological neurons: A case study of an invertebrate pacemaker neuron

Computational modeling of biological neurons allows for exploration of many parameter combinations and various types of neuronal activity, without requiring a prohibitively large number of “wet” experiments. On the other hand, analysis and biological interpretation of such, often very extensive, databases of models can be difficult. In this article, we present two Computational Intelligence (CI) approaches, based on Artificial Neural Networks (ANN) and Multi-Objective Evolutionary Algorithms (MOEA), that we have successfully applied to the problem of analysis and interpretation of model neuronal data.

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