Training spiking neural models using cuckoo search algorithm

Abstract

Several meta-heuristic algorithms have been pro posed in the last years for solving a wide range of optimization problems. Cuckoo Search Algorithm (CS) is a novel meta heuristic based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behavior of some birds and fruit flies. This algorithm has been applied in a wide range of optimization problems; nonetheless, their promising results suggest its application in the field of artificial neural networks, specially during the adjustment of the synaptic weights. On the other hand, spiking neurons are neural models that try to simulate the behavior of biological neurons when they are excited with an input current (input pattern) during a certain period time. Instead of generating a response in its output every iteration, as classical neurons do, this model generates a response (spikes or spike train) only when the model reaches a specific threshold. This response could be coded into a firing rate and perform a pattern classification task according to the firing rate generated with the input current. To perform a classification task the model ought to exhibit the next behavior: patterns from the same class must generate similar firing rates and patterns from other classes have to generate firing rates sufficiently dissimilar to differentiate among the classes. The model needs of a training phase aimed to adjust their synaptic weights and exhibit the desired behavior. In this paper, we describe how the CS algorithm can be useful to train a spiking neuron to be applied in a pattern classification task. The accuracy of the methodology is tested using several pattern recognition problems.