Abstract:
The Bat Algorithm (BA) is a meta-heuristic algorithm based on echolocation behavior of microbats. The authors propose BA based Spiking Neural Network (SNN) model, where the advantages of BA and efficiency of SNN are exploited for classification tasks using some benchmark datasets. The advantages of the BA have been well exploited in the Artificial Neural Networks (ANN) domain particularly with the adjustment of weights. We therefore, leveraged on the BA as a learning strategy to train an SNN using the Leaky Integrate and Fire (LIF) and Izhikevich models to solve non-linear pattern classification tasks. In order to successfully discriminate between the various classes, the models are trained to fire at the same or similar firing rate for inputs from the same class, and inputs patterns from different classes to also spike or fire at different rate. To justify how efficient and how powerful the proposed model is, only one neuron is used. Finally, the model is tested on different non-linear pattern recognition tasks and comparison is made between our model and other similar existing models and our proposed model outperformed some of the state-of-the-art-models. To the best of our knowledge, this is the first work to implement BA in SNN
