Odor pattern recognition of a novel bio-inspired olfactory neural network based on kernel clustering

Abstract

Olfactory system is an important component in sensory nervous system. When an olfactory receptor receives odor stimulation, it transfers chemical signals into electrical signals, and delivers to the olfactory bulb, where integrates and codes the olfactory information, further to the cerebral olfactory cortex to generate olfaction. Establishment of a novel bio-inspired olfactory neural network for the study of olfactory information processing is helpful to understand how olfactory system can effectively differentiate different types and concentrations of odor. Based on a traditional olfactory bulb model composed of mitral cells, granule cells, and periglomerular cells, olfactory cortex was presented to establish a novel bio-inspired olfactory neural network model. Meanwhile, inhibitory synaptic plasticity was considered when network was receiving stimulation. The results of simulation indicated that inhibitory synaptic plasticity could balance excitatory and inhibitory synaptic currents in the olfactory cortex with specific firing patterns under odor stimulation. Olfactory cortex showed different firing patterns with different odor stimulations, and also presented similar firing patterns and different firing strengths for the same type of odor at different concentrations. Meanwhile, based on hierarchical clustering and fuzzy clustering, recognition of pure odors and mixed odors could be realized.