Synaptic plasticity is studied herein using a voltage-driven memristor model. The bidirectional weight update technique is demonstrated, and significant synaptic features, including nonlinear and threshold-based learning and long-term potentiation and long-term depression, are emulated. The spike-timing-dependent plasticity (STDP) learning characteristic curve is obtained from exhaustive simulations. Then, using leaky integrate and fire neurons and memristive synapses, fully connected spiking neural networks with \(2\times 2\) and \(4\times 2\) architectures are constructed, and unsupervised learning using the STDP rule and winner-takes-all strategy is evaluated in those networks for pattern classification.

本文使用电压驱动忆阻器模型研究突触可塑性。演示了双向权重更新技术，并仿真了重要的突触功能，包括非线性和基于阈值的学习以及长期增强和长期抑制。从详尽的仿真中获得了依赖于尖峰时间的可塑性（STDP）学习特性曲线。然后，使用泄漏积分并激发神经元和忆阻突触，构造具有\（2 \ times 2 \）和\（4 \ times 2 \）架构的完全连接的尖峰神经网络，并使用STDP规则和获胜者进行无监督学习-在这些网络中评估所有策略以进行模式分类。