Hierarchical classification of odor quality based on dynamical property of neural network of olfactory cortex

We study a mechanism of odor classification in the olfactory cortex based on the hypothesis that components of an odor and their mixing ratio are encoded into a temporal sequence of spatial activity patterns, that is, a limit cycle attractor in the olfactory bulb. We present a functional network model of the olfactory cortex which consists of three, rostral, middle, and caudal, compartments. When a temporal sequence of spatial firing patterns is injected from the olfactory bulb to the network model, neural activity states of rostral, middle and caudal compartments are fixed to the spatial patterns corresponding to the strong, middle and weak components, respectively. Each compartment recognizes a relevant odorant component. An odor quality is recognized based on the combination of three fixed patterns. The stronger the mixing ratio of a component is, the earlier the component is recognized. This is a hierarchical classification