Decision Making by a Neuromorphic Network of Volatile Resistive Switching Memories

TL;DR

This paper demonstrates a neuromorphic system using volatile resistive RAMs that mimics biological decision-making and addresses the Two-Alternative Forced Choice problem through noisy perception processing.

Contribution

It introduces a novel neuromorphic architecture based on volatile-RRAMs capable of biological decision-making behavior.

Findings

Successfully mimics biological decision-making processes

Addresses the Two-Alternative Forced Choice problem

Utilizes volatile-RRAMs with tunable retention times

Abstract

The necessity of having an electronic device working in relevant biological time scales with a small footprint boosted the research of a new class of emerging memories. Ag-based volatile resistive switching memories (RRAMs) feature a spontaneous change of device conductance with a similarity to biological mechanisms. They rely on the formation and self-disruption of a metallic conductive filament through an oxide layer, with a retention time ranging from a few milliseconds to several seconds, greatly tunable according to the maximum current which is flowing through the device. Here we prove a neuromorphic system based on volatile-RRAMs able to mimic the principles of biological decision-making behavior and tackle the Two-Alternative Forced Choice problem, where a subject is asked to make a choice between two possible alternatives not relying on a precise knowledge of the problem, rather on noisy perceptions.