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The current study uses a novel method of multilevel neurons and high ordersynchronization effects described by a family of special metrics, for patternrecognition in an oscillatory neural network (ONN). The output oscillator(neuron) of the network has multilevel variations in its synchronization valuewith the reference oscillator, and allows classification of an input patterninto a set of classes. The ONN model is implemented on thermally-coupledvanadium dioxide oscillators. The ONN is trained by the simulated annealingalgorithm for selection of the network parameters. The results demonstrate thatONN is capable of classifying 512 visual patterns (as a cell array 3 * 3,distributed by symmetry into 102 classes) into a set of classes with a maximumnumber of elements up to fourteen. The classification capability of the networkdepends on the interior noise level and synchronization effectivenessparameter. The model allows for designing multilevel output cascades of neuralnetworks with high net data throughput. The presented method can be applied inONNs with various coupling mechanisms and oscillator topology.

A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing