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Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction

Engineering and Technology

Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction

C. Gao, D. Liu, et al.

This groundbreaking research showcases an ultra-short channel organic neuromorphic vertical transistor that utilizes a distributed reservoir with 1152 states for advanced recognition and prediction capabilities. With over 94% recognition accuracy and over 95% prediction correlation, this device represents a significant leap in reservoir computing networks, led by a team of innovative researchers.

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Playback language: English
Abstract
This paper reports an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction. The device, acting as a reservoir, maps sequential signals into a distributed reservoir state space with 1152 states. Grouped-reservoir computing based on this device achieves over 94% recognition accuracy and over 95% prediction correlation, offering a new strategy for high-performance reservoir computing networks.
Publisher
Nature Communications
Published On
Jan 25, 2024
Authors
Changsong Gao, Di Liu, Chenhui Xu, Weidong Xie, Xianghong Zhang, Junhua Bai, Zhixian Lin, Cheng Zhang, Yuanyuan Hu, Tailiang Guo, Huipeng Chen
Tags
organic neuromorphic transistor
reservoir computing
recognition accuracy
prediction correlation
device performance

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