Electronic system with memristive synapses for pattern recognition.
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.
Department of Nanobio Materials and Electronics, Gwangju Institute of Science and Technology, Gwangju, Korea 500-712.
Sangsu
S
Park
Department of Mechatronics, Gwangju Institute of Science and Technology, Gwangju, Korea 500-712.
Myonglae
M
Chu
Department of Medical System Engineering, Gwangju Institute of Science and Technology Gwangju, Korea 500-712.
Jongin
J
Kim
Department of Nanobio Materials and Electronics, Gwangju Institute of Science and Technology, Gwangju, Korea 500-712.
Jinwoo
J
Noh
School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Korea 500-712.
Moongu
M
Jeon
Department of Nanobio Materials and Electronics, Gwangju Institute of Science and Technology, Gwangju, Korea 500-712.
Byoung
B
Hun Lee
Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang, Korea 790-784.
Hyunsang
H
Hwang
Department of Medical System Engineering, Gwangju Institute of Science and Technology Gwangju, Korea 500-712.
Boreom
B
Lee
Department of Mechatronics, Gwangju Institute of Science and Technology, Gwangju, Korea 500-712.
Byung-geun
BG
Lee
D004569:Electroencephalography; D004581:Electronics / Q000379:methods*; D006801:Humans; D007092:Imagination; D007858:Learning; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013060:Speech; D013569:Synapses / Q000502:physiology*