Beyond Memristor: Neuromorphic Computing Using Meminductor

TL;DR

This paper introduces a meminductor based on magnetic coils, demonstrating its potential in neuromorphic computing and brain-inspired systems, with experimental validation using amoeba behavior modeling.

Contribution

It presents the concept, theoretical foundation, and experimental realization of a meminductor, expanding beyond memristor-based architectures for neuromorphic applications.

Findings

Meminductor's inductance depends on charge, influenced by magnetic core magnetization.

Experimental meminductor reproduces amoeba's memorizing and timing behaviors.

Proposes a new computing paradigm beyond memristor-based systems.

Abstract

Memristor (resistor with memory), inductor with memory (meminductor) and capacitor with memory (memcapacitor) have different roles to play in novel computing architectures. We found that a coil with a magnetic core is an inductor with memory (meminductor) in terms of its inductance L(q) being a function of the charge q. The history of the current passing through the coil is remembered by the magnetization inside the magnetic core. Such a meminductor can play a unique role (that cannot be played by a memristor) in neuromorphic computing, deep learning and brain inspired since the time constant of a neuromorphic RLC circuit is jointly determined by the inductance and capacitance, rather than the resistance. As an experimental verification, this newly invented meminductor was used to reproduce the observed biological behaviour of amoebae (the memorizing, timing and anticipating mechanisms). In conclusion, a beyond memristor computing paradigm is theoretically sensible and experimentally practical.