Neuromorphic systems that employ advanced synaptic learning rules, such as the three-factor learning rule, require synaptic devices of increased complexity. Herein, a novel neoHebbian artificial synapse utilizing ReRAM devices has been proposed and experimentally validated to meet this demand. This synapse features two distinct state variables: a neuron coupling weight and an "eligibility trace" that dictates synaptic weight updates. The coupling weight is encoded in the ReRAM conductance, while the "eligibility trace" is encoded in the local temperature of the ReRAM and is modulated by applying voltage pulses to a physically co-located resistive heating element. The utility of the proposed synapse has been investigated using two representative tasks: first, temporal signal classification using Recurrent Spiking Neural Networks (RSNNs) employing the e-prop algorithm, and second, Reinforcement Learning (RL) for path planning tasks in feedforward networks using a modified version of the same learning rule. System-level simulations, accounting for various device and system-level non-idealities, confirm that these synapses offer a robust solution for the fast, compact, and energy-efficient implementation of advanced learning rules in neuromorphic hardware.

翻译：采用先进突触学习规则（如三因子学习规则）的神经形态系统需要复杂度更高的突触器件。为此，本文提出并实验验证了一种利用ReRAM器件的新型neoHebbian人工突触以满足该需求。该突触具有两个独立的状态变量：神经元耦合权重与决定突触权重更新的"资格迹"。耦合权重通过ReRAM电导编码，而"资格迹"则通过ReRAM局部温度编码，并通过向物理共置的电阻加热元件施加电压脉冲进行调制。通过两项代表性任务研究了所提出突触的实用性：首先，采用e-prop算法的循环脉冲神经网络（RSNN）进行时序信号分类；其次，在前馈网络中使用改进版相同学习规则进行路径规划任务的强化学习（RL）。考虑器件与系统层面多种非理想因素的系统级仿真证实，这些突触为神经形态硬件中先进学习规则的快速、紧凑且高能效实现提供了稳健解决方案。