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

TL;DR

This paper introduces a novel oscillatory neural network model using multilevel neurons and high-order synchronization effects, enabling efficient pattern recognition and classification of visual patterns with high throughput.

Contribution

It presents a new ONN model with multilevel synchronization-based neurons implemented on VO2 oscillators, enhancing pattern classification capabilities.

Findings

Classified 512 visual patterns into 102 classes.

Achieved maximum classification of 14 elements per class.

Demonstrated dependence of classification on noise and synchronization parameters.

Abstract

The current study uses a novel method of multilevel neurons and high order synchronization effects described by a family of special metrics, for pattern recognition in an oscillatory neural network (ONN). The output oscillator (neuron) of the network has multilevel variations in its synchronization value with the reference oscillator, and allows classification of an input pattern into a set of classes. The ONN model is implemented on thermally-coupled vanadium dioxide oscillators. The ONN is trained by the simulated annealing algorithm for selection of the network parameters. The results demonstrate that ONN 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 maximum number of elements up to fourteen. The classification capability of the network depends on the interior noise level and synchronization effectiveness parameter. The model allows for designing multilevel output cascades of neural networks with high net data throughput. The presented method can be applied in ONNs with various coupling mechanisms and oscillator topology.