An olfactory recognition model based on spatio-temporal encoding of odor quality in the olfactory bulb

Abstract.

In order to study the problem how the olfactory neural system processes the odorant molecular information for constructing the olfactory image of each object, we present a dynamic model of the olfactory bulb constructed on the basis of well-established experimental and theoretical results. The information relevant to a single odor, i.e. its constituent odorant molecules and their mixing ratios, are encoded into a spatio-temporal pattern of neural activity in the olfactory bulb, where the activity pattern corresponds to a limit cycle attractor in the mitral cell network. The spatio-temporal pattern consists of a temporal sequence of spatial firing patterns: each constituent molecule is encoded into a single spatial pattern, and the order of magnitude of the mixing ratio is encoded into the temporal sequence. The formation of a limit cycle attractor under the application of a novel odor is carried out based on the intensity-to-time-delay encoding scheme. The dynamic state of the olfactory bulb, which has learned many odors, becomes a randomly itinerant state in which the current firing state of the bulb itinerates randomly among limit cycle attractors corresponding to the learned odors. The recognition of an odor is generated by the dynamic transition in the network from the randomly itinerant state to a limit cycle attractor state relevant to the odor, where the transition is induced by the short-term synaptic changes made according to the Hebbian rule under the application of the odor stimulus.