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This paper is a report of a study in progress that considers development of a framework and environment for modelling hardware memristor-based neural networks. An extensive review of the domain has been performed and partly reported in this work. Fundamental papers on memristors and memristor related technologies have been given attention. Various physical implementations of memristors have mentioned together with several mathematical models of the metal-dioxide memristor group. One of the latter has been given a closer look in the paper by briefly describing model’s mechanisms and some of the important observations. The paper also considers a recently proposed architecture of memristor-based neural networks and suggests enhancing it by replacing the utilized memristor model with a more accurate one. Based on this review, a number of development requirements was derived and formally specified. Ontological and functional models of the domain at hand have been proposed to foster understanding of the corresponding field from different points of view. Ontological model is supposed to shed light onto the object-oriented structure of memristor-based neural network, whereas the functional model exposes the underlying behavior of network’s components which is described in terms of mathematical equations. Finally, the paper shortly speculates about the development platform for the framework and its prospects.

Memristor-based Hardware Neural Networks Modelling Review and Framework Concept