An application of small-world cellular neural networks on odor classification

Abstract

Many biological networks are constructed with both regular and random connections between neurons. Bio-inspired systems should prevent this mixed topology of biological networks while the artificial system is still realizable. In this work, a bio-inspired network which has many analog realizations, Cellular Neural Network (CNN) is investigated under existing random connections in addition to its regular connections: Small-World Cellular Neural Network (SWCNN). Antennal Lobe, an organ in the olfaction system of insects, is modeled with SWCNN by extending the network with the use of two types of processors on the same network. The model combined with a classifier, SVM and overall system is tested with a five-class odor classification problem. While all neurons are connected to each other with direct or indirect connections in CNNs, the idea of short-cuts does not provide an improvement in classification performance but the results show that the fault tolerance ability of SWCNN is better than the classical CNN.