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.
Highlights
IntroductionResearch on oscillatory neural network (ONN) is mainly based on phase oscillator models
Hypotheses about the functional importance of synchronization for information processed by the brain were put forward long ago [1,2] and its experimental discovery [3,4] encouraged the creation of neural networks models with oscillatory dynamics [5,6,7] and neuromorphic algorithms of image processing based on synchronization [8,9,10,11].Research on oscillatory neural network (ONN) is mainly based on phase oscillator models
We studied the ONN of thermally-coupled VO2 -oscillators and present a general concept of visual pattern recognition based on high order synchronization effects
Summary
Research on ONN is mainly based on phase oscillator models. The problem of pattern recognition in ONN has been intensively studied [16,17], and two main global synchronization methods have been outlined: frequency-shift [10] and phase-shift [11]. The phase-shift keying method of encoding [11] enables storage of more than one pattern by certain combinations of phase shift at the same weight ONN matrix. This method has the following drawbacks: N2 couplings with tunable weights and a two-stage procedure of pattern recognition
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