Electronic system with memristive synapses for pattern recognition.

Sangsu Park,Boreom Lee,Jinwoo Noh,Hyunsang Hwang,Jongin Kim,Myonglae Chu,Moongu Jeon,Byoung Hun Lee,Byung-Geun Lee

doi:10.1038/srep10123

Abstract

Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.

Highlights

Using a simple crossbar array[13]
We present experimental results from a memristive hardware neural network (HNN) system that recognizes the human thought pattern relating to three vowels, /a/, /i/, and /u/, based on electroencephalography (EEG) signals generated while a subject imagines speaking these vowels
As shown in fig. 1, the proposed system can be divided into two functional blocks

Summary

Introduction

Using a simple crossbar array[13]. The conductance of our memristive synapse changes more gradually and symmetrically in the presence of voltage pulses above a certain threshold voltage; otherwise, the memristive synapse retains its conductance. We present experimental results from a memristive HNN system that recognizes the human thought pattern relating to three vowels, /a/, /i/, and /u/, based on electroencephalography (EEG) signals generated while a subject imagines speaking these vowels. We believe that this is the first primitive prototype of an electronic system that utilizes a cross-point memristive synapse array for EEG pattern recognition. Our results suggest a new research direction for memristive HNNs

Methods

Results

Conclusion