Invariant recognition of spatio-temporal patterns in the olfactory system model

This paper presents a model of a network of integrate-and-fire neurons with time delay weights, capable of invariant spatio-temporal pattern recognition. Spatio-temporal patterns are formed by spikes according to the encoding principle that the phase shifts of the spikes encode the input stimulus intensity which corresponds to the concentration of constituent molecules of an odor. We applied the Hopfield's phase shift encoding principle at the output level for spatio-temporal pattern recognition: Firing of an output neuron indicates that corresponding odor is recognized and phase shift of its firing encodes the concentration of the recognized odor. The temporal structure of the model provides the base for the modeling of higher level tasks, where temporal correlation is involved, such as feature binding and segmentation, object recognition, etc.