As an extension of the pairwise spike-timing-dependent plasticity (STDP) learning rule, the triplet STDP is provided with greater capability in characterizing the synaptic changes in the biological neural cell. In this work, a novel mixed-signal circuit scheme, called multiple-step quantized triplet STDP, is designed to provide a precise and flexible implementation of coactivation triplet STDP learning rule in memristive synapse spiking neural network. The robustness of the circuit is greatly improved through the utilization of pulse-width encoded weight modulation signals. The circuit performance is studied through the simulations which are carried out in MATLAB Simulink & Simscape, and assessment is given by comparing the results of circuits with the algorithmic approaches.

翻译：作为成对脉冲时序依赖可塑性学习规则的扩展，三脉冲STDP在刻画生物神经细胞突触变化方面具有更强的能力。本文设计了一种新颖的混合信号电路方案——多步量化三脉冲STDP，用于在忆阻突触脉冲神经网络中精确且灵活地实现共激活三脉冲STDP学习规则。通过采用脉宽编码权重调制信号，电路的鲁棒性得到显著提升。基于MATLAB Simulink & Simscape平台进行的仿真研究评估了电路性能，并通过将电路结果与算法方法进行对比给出了相应评估。