Tuning the parameters of an integrate and fire neuron via a genetic algorithm for solving pattern recognition problems

Recently, it has been proven that spiking neurons can be used for some pattern recognition problems. Nonetheless, the spiking neurons models have many parameters that have to be manually adjusted in order to achieve the desired behavior. This paper has the purpose of showing an optimization method for one such model, the Integrate & Fire spiking model (I&F). A genetic algorithm (GA) is proposed to automatically adjust the parameters, removing the need of manual tuning and increasing efficiency. Initial experimentation is done by tuning the I&F model parameters by hand, to confirm the importance and relevance of determining the best parameter values. The GA is then used to automatically tune different parameter combinations of the pattern recognition model, which uses the I&F neuron as core, to determine which parameters are worth including in the GA. The proposed method was tested with five different datasets, where no change was required to apply the model to each. Very good results were achieved in all test cases, but experiments where parameters of the neuron model were included converged faster.