Quasiperiodic AlGaAs superlattices for neuromorphic networks and nonlinear control systems

TL;DR

This paper proposes using quasiperiodic AlGaAs superlattices as nonlinear elements in neuromorphic networks, demonstrating improved image filtering and multistability, with potential applications in neural modeling and control systems.

Contribution

It introduces the use of Fibonacci and figurate quasiperiodic superlattices as nonlinear components in neuromorphic networks, showing enhanced filtering and multistability over traditional diodes.

Findings

Fibonacci and figurate superlattices improve image filtering accuracy.

Figurate superlattice F011(1) reduces filtration error by nearly half.

Quasiperiodic superlattices exhibit multistability and complex voltage-current characteristics.

Abstract

The application of quasiperiodic AlGaAs superlattices as a nonlinear element of the FitzHugh-Nagumo neuromorphic network is proposed and theoretically investigated on the example of Fibonacci and figurate superlattices. The sequences of symbols for the figurate superlattices were produced by decomposition of the Fibonacci superlattices' symbolic sequences. A length of each segment of the decomposition was equal to the corresponding figurate number. It is shown that a nonlinear network based upon Fibonacci and figurate superlattices provides better parallel filtration of a half-tone picture than a network based upon traditional diodes which have cubic voltage-current characteristics. It was found that the figurate superlattice F011(1) as a nonlinear network's element provides the filtration error almost twice less than the conventional "cubic" diode. These advantages are explained by a wavelike shape of the decreasing part of the quasiperiodic superlattice's voltage-current characteristic, which leads to multistability of the network's cell. This multistability promises new interesting nonlinear dynamical phenomena. A variety of wavy forms of voltage-current characteristics opens up new interesting possibilities for quasiperiodic superlattices and especially for figurate superlattices in many areas - from nervous system modeling to nonlinear control systems development