Analog-circuit implementation of multiplicative spike-timing-dependent plasticity with linear decay
Analog-circuit implementation of multiplicative spike-timing-dependent plasticity with linear decay
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DOI：
10.1587/nolta.12.685
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发表时间：
2021-01-01
影响因子：
0.5
通讯作者：
Madrenas, Jordi
中科院分区：
文献类型：
--
作者：
Moriya, Satoshi;Kato, Tatsuki;Madrenas, Jordi
神经形态工程是下一代信息和通信技术中一种很有前途的计算范式。特别是，尖峰神经网络预计将大大降低功耗，由于其事件驱动的操作。尖峰时间依赖可塑性（STDP）规则，从尖峰神经元之间的局部尖峰时间差异学习，是一种生物学上合理的尖峰神经网络（SNN）学习规则。在这项研究中，我们设计并模拟了一个模拟电路，再现乘法STDP规则，这是更灵活和适应外部信号。我们还推导出所提出的电路的行为的解析表达式。这些结果为设计用于边缘计算等应用的节能神经形态设备提供了重要见解。
Neuromorphic engineering is a promising computing paradigm in next-generation information and communication technology. In particular, spiking neural networks are expected to reduce power consumption drastically owing to their event-driven operation. The spike-timing-dependent plasticity (STDP) rule, which learns from local spike-timing differences between spiking neurons, is a biologically plausible learning rule for spiking neural networks (SNNs). In this study, we designed and simulated an analog circuit that reproduces the multiplicative STDP rule, which is more flexible and adaptive to external signals. We also derived analytical expressions for the behavior of the proposed circuit. These results provide important insights for designing energy efficient neuromorphic devices for applications including edge computing.