标题： 基于Bi2O2Se的三端忆阻器中真正伴随且独立表达的短期和长期可塑性 显示英文标题

标题： Truly concomitant and independently expressed short- and long-term plasticity in Bi2O2Se-based three-terminal memristor

摘要： 在不同时间尺度上多种突触可塑性机制的协同作用产生了复杂的认知过程。 为了在忆阻类脑神经形态系统中实现相当的认知复杂性，能够同时模拟短期可塑性（STP）和长期可塑性（LTP）的器件是必不可少的。 然而，由于无法通过不同的位点和机制进行解耦，现有的忆阻器中尚未报道这种基本的仿生特性，其中STP和LTP只能被选择性地诱导。 在这项工作中，我们首次展示了三端忆阻器中真正同时存在的STP和LTP，该器件使用独立的物理现象来表示每种形式的可塑性。 新兴的层状材料Bi2O2Se首次被用于忆阻器，为超薄、高速和低功耗的神经形态器件开辟了前景。 我们忆阻器中STP和LTP的协同作用可以通过刺激频率或强度实现从抑制到增强的全范围瞬时突触效能调节，为神经形态功能的实现提供了一个多功能的器件平台。 开发了一种循环神经电路模型来模拟复杂的“睡眠-觉醒周期自调节”过程，其中STP和LTP的同时存在被提出为实现这种神经稳态的关键因素。 这项工作为忆阻器高度复杂的计算能力及其在实现先进神经形态功能方面的前景提供了新的见解。 显示英文摘要

摘要： Orchestration of diverse synaptic plasticity mechanisms across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive neuromorphic systems, devices that are capable to emulate short- and long-term plasticity (STP and LTP, respectively) concomitantly are essential. However, this fundamental bionic trait has not been reported in any existing memristors where STP and LTP can only be induced selectively because of the inability to be decoupled using different loci and mechanisms. In this work, we report the first demonstration of truly concomitant STP and LTP in a three-terminal memristor that uses independent physical phenomena to represent each form of plasticity. The emerging layered material Bi2O2Se is used in memristor for the first time, opening up the prospects for ultra-thin, high-speed and low-power neuromorphic devices. The concerted action of STP and LTP in our memristor allows full-range modulation of the transient synaptic efficacy, from depression to facilitation, by stimulus frequency or intensity, providing a versatile device platform for neuromorphic function implementation. A recurrent neural circuitry model is developed to simulate the intricate "sleep-wake cycle autoregulation" process, in which the concomitance of STP and LTP is posited as a key factor in enabling this neural homeostasis. This work sheds new light on the highly sophisticated computational capabilities of memristors and their prospects for realization of advanced neuromorphic functions.