High-Order Associative Learning Based on Memristive Circuits for Efficient Learning

TL;DR

This paper presents a high-order memristive associative learning framework that mimics biological learning, significantly improves efficiency, and demonstrates scalability in image recognition tasks with low power consumption.

Contribution

Introduces a biologically realistic high-order memristive learning framework that enhances efficiency and scalability in associative learning applications.

Findings

230% improvement in learning efficiency in Pavlov's experiments

Memristor power consumption remains below 11 μW

Achieves 100% accuracy in large-scale image recognition

Abstract

Memristive associative learning has gained significant attention for its ability to mimic fundamental biological learning mechanisms while maintaining system simplicity. In this work, we introduce a high-order memristive associative learning framework with a biologically realistic structure. By utilizing memristors as synaptic modules and their state information to bridge different orders of associative learning, our design effectively establishes associations between multiple stimuli and replicates the transient nature of high-order associative learning. In Pavlov's classical conditioning experiments, our design achieves a 230% improvement in learning efficiency compared to previous works, with memristor power consumption in the synaptic modules remaining below 11 {\mu}W. In large-scale image recognition tasks, we utilize a 20*20 memristor array to represent images, enabling the system to recognize and label test images with semantic information at 100% accuracy. This scalability across different tasks highlights the framework's potential for a wide range of applications, offering enhanced learning efficiency for current memristor-based neuromorphic systems.