Functional formation of a neuromorphic reservoir computational element based on a memristive metamaterial

A neural network structure is designed based on the ability of a certain class of calculators to recombine internal resources in order to produce neuromorphic elements to solve applied problems. This approach is rooted in a composite material with controlled local conductivity to form volumetric inhomogeneities capable of responding to and influencing external electrostatic effects. Such compounds aggregate into stable clusters suitable for modelling the processes that occur during information processing in natural neuronal entities. The use of conductive transitions between substrate-formed neuromorphic clusters as a learning structure makes it possible to increase the reliability of the neural network system. Long-term, non-volatile storage of information about the elements of the training sample in variable structures is possible. The basic approach to information conversion is to manage the electrostatic influence as it passes through the layered structures formed. The response to the input is not formed by propagating the signal through conductive elements with variable conductivity, but by passing the energy impact through a limited volume of metamaterial. Thus, a massively parallel processing of information can be achieved with the implementation of a mechanism for combining the opinions of independent neural network clusters that influence the final decision. Furthermore, this method of spreading effects in such an environment greatly simplifies the process of adding elements to the neural network. The lack of direct electrical interconnection facilitates the easy addition of new computational elements without significant rearrangement of the conductive media. Networks of this type are capable of significant growth without loss of experience. The input conversion process using modified delta coding prevents premature wear and tear on reconfigurable network elements. The manner in which information is presented and the manner in which neural network computing is organised enabled the creation of limited autonomous oscillations within the volume of the calculator to maintain circulating memory and the ability to gradually accumulate network experience for its subsequent recording in configurable elements. The identified features resulted in the application of this kind of calculators in the task of developing radio frequency management plans for the organisation of stable communication in a complex electromagnetic environment.