Yao Yao, Pankow Robert M, Huang Wei, Wu Cui, Gao Lin, Cho Yongjoon, Chen Jianhua, Zhang Dayong, Sharma Sakshi, Liu Xiaoxue, Wang Yuyang, Peng Bo, Chung Sein, Cho Kilwon, Fabiano Simone, Ye Zunzhong, Ping Jianfeng, Marks Tobin J, Facchetti Antonio
Department of Chemistry and the Materials Research Center, Northwestern University, Evanston, IL 60208.
School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Proc Natl Acad Sci U S A. 2025 Jan 14;122(2):e2414879122. doi: 10.1073/pnas.2414879122. Epub 2025 Jan 8.
Human perception systems are highly refined, relying on an adaptive, plastic, and event-driven network of sensory neurons. Drawing inspiration from Nature, neuromorphic perception systems hold tremendous potential for efficient multisensory signal processing in the physical world; however, the development of an efficient artificial neuron with a widely calibratable spiking range and reduced footprint remains challenging. Here, we report an efficient organic electrochemical neuron (OECN) with reduced footprint (<37 mm) based on high-performance vertical OECT (vOECT) complementary circuitry enabled by an advanced n-type polymer for balanced p-/n-type vOECT performance. The OECN exhibits outstanding neuronal characteristics, capable of producing spikes with a widely calibratable state-of-the art firing frequency range of 0.130 to 147.1 Hz. Leveraging this capability, we develop a neuromorphic perception system that integrates mechanical sensors with the OECN and integrates them with an artificial synapse for tactile perception. The system successfully encodes tactile stimulations into frequency-dependent spikes, which are further converted into postsynaptic responses. This bioinspired design demonstrates significant potential to advance cyborg and neuromorphic systems, providing them with perceptual capabilities.
人类感知系统高度精细,依赖于由感觉神经元组成的适应性、可塑性且受事件驱动的网络。受自然启发,神经形态感知系统在物理世界中进行高效多感官信号处理方面具有巨大潜力;然而,开发一种具有广泛可校准尖峰范围且占地面积更小的高效人工神经元仍然具有挑战性。在此,我们报告了一种基于高性能垂直有机电化学晶体管(vOECT)互补电路的高效有机电化学神经元(OECN),其占地面积减小(<37平方毫米),该互补电路由一种先进的n型聚合物实现,以实现平衡的p型/n型vOECT性能。该OECN展现出卓越的神经元特性,能够产生尖峰,其最先进的激发频率范围为0.130至147.1赫兹,且可广泛校准。利用这一能力,我们开发了一种神经形态感知系统,该系统将机械传感器与OECN集成,并将它们与用于触觉感知的人工突触集成。该系统成功地将触觉刺激编码为频率相关的尖峰,这些尖峰进一步转换为突触后反应。这种受生物启发的设计展示了推进半机械人和神经形态系统发展的巨大潜力,为它们赋予感知能力。