Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.
School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China.
Nano Lett. 2024 Sep 4;24(35):10865-10873. doi: 10.1021/acs.nanolett.4c02470. Epub 2024 Aug 14.
Threshold switching (TS) memristors are promising candidates for artificial neurons in neuromorphic systems. However, they often lack biological plausibility, typically functioning solely in an excitation mode. The absence of an inhibitory mode limits neurons' ability to synergistically process both excitatory and inhibitory synaptic signals. To address this limitation, we propose a novel memristive neuron capable of operating in both excitation and inhibition modes. The memristor's threshold voltage can be reversibly tuned using voltages of different polarities because of its bipolar TS behavior, enabling the device to function as an electronically reconfigurable bi-mode neuron. A variety of neuronal activities such as all-or-nothing behavior and tunable firing probability are mimicked under both excitatory and inhibitory stimuli. Furthermore, we develop a self-adaptive neuromorphic vision sensor based on bi-mode neurons, demonstrating effective object recognition in varied lighting conditions. Thus, our bi-mode neuron offers a versatile platform for constructing neuromorphic systems with rich functionality.
门限切换(TS)忆阻器是神经形态系统中人工神经元的有前途的候选者。然而,它们通常缺乏生物学合理性,通常仅在兴奋模式下工作。抑制模式的缺失限制了神经元协同处理兴奋性和抑制性突触信号的能力。为了解决这一限制,我们提出了一种新型的忆阻神经元,能够在兴奋和抑制两种模式下工作。由于其双极 TS 行为,忆阻器的门限电压可以使用不同极性的电压进行可逆调节,从而使该器件能够作为可电子重配置的双模式神经元工作。在兴奋性和抑制性刺激下,该器件可以模拟各种神经元活动,如全有或全无行为和可调的发射概率。此外,我们基于双模式神经元开发了一种自适应神经形态视觉传感器,在不同的光照条件下实现了有效的目标识别。因此,我们的双模式神经元为构建具有丰富功能的神经形态系统提供了一个通用的平台。