Evolving spiking neural networks for taste recognition

The paper investigates the use of the spiking neural networks for taste recognition in a simple artificial gustatory model. We present an approach based on simple integrate-and-fire neurons with rank order coded inputs where the network is built by an evolving learning algorithm. Further, we investigate how the information encoding in a population of neurons influences the performance of the networks. The approach is tested on two real-world datasets where the effectiveness of the population coding and networkpsilas adaptive properties are explored.