Central pattern generating and recognizing in olfactory bulb: A correlation learning rule

Abstract A learning rule called an input correlation rule that can simplify exploration of the behavior of learning, generating, and classifying patterns in the vertebrate olfactory system is proposed. We apply this correlation rule to a set of fully interconnected coupled oscillators that comprises a dynamical model of the olfactory bulb so as to form “templates” of oscillators with strengthened interconnection in respect to inputs classed as “learned.” We obtain a content addressable memory in which phase coherent oscillation provides for central pattern generation and recognition. We use this analog model neural network to simulate dynamic features of the olfactory bulb in detail by numerical integration and multivariate analysis. The model classifies 100% correctly for incomplete inputs, testing inputs about their training centroids, and distortion by noise that is defined as input to nontemplate elements. The model also allows substantially overlapping templates, which implies that it possesses a large information capacity. For multiple inputs the model gives correct output of the forms A and B, A and not B, B and not A, or neither. The initial conditions of the model at the time of onset of input play no role in classification. Classification is achieved within 20 to 50 ms of simulated run time, even though convergence to a limit cycle requires up to 10 cycles (200 ms). The repetition rate of convergence from one pattern to the next in the model exceeds 10 patterns/s.

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