Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, Renmin Street, 5268, Changchun, China.
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy.
Nat Commun. 2020 Mar 20;11(1):1510. doi: 10.1038/s41467-020-15158-3.
The close replication of synaptic functions is an important objective for achieving a highly realistic memristor-based cognitive computation. The emulation of neurobiological learning rules may allow the development of neuromorphic systems that continuously learn without supervision. In this work, the Bienenstock-Cooper-Munro learning rule, as a typical case of spike-rate-dependent plasticity, is mimicked using a generalized triplet-spike-timing-dependent plasticity scheme in a WO memristive synapse. It demonstrates both presynaptic and postsynaptic activities and remedies the absence of the enhanced depression effect in the depression region, allowing a better description of the biological counterpart. The threshold sliding effect of Bienenstock-Cooper-Munro rule is realized using a history-dependent property of the second-order memristor. Rate-based orientation selectivity is demonstrated in a simulated feedforward memristive network with this generalized Bienenstock-Cooper-Munro framework. These findings provide a feasible approach for mimicking Bienenstock-Cooper-Munro learning rules in memristors, and support the applications of spatiotemporal coding and learning using memristive networks.
实现高度逼真的基于忆阻器的认知计算的一个重要目标是紧密复制突触功能。神经生物学学习规则的仿真可以使连续无监督学习的神经形态系统得到发展。在这项工作中，Bienenstock-Cooper-Munro 学习规则作为一种典型的尖峰率依赖性可塑性，使用广义三重尖峰定时依赖可塑性方案在 WO 忆阻突触中进行模拟。它同时模拟了突触前和突触后的活动，并纠正了在抑制区域中缺乏增强抑制效应的问题，从而可以更好地描述生物对应物。Bienenstock-Cooper-Munro 规则的阈值滑动效应是使用二阶忆阻器的历史相关特性来实现的。使用这种广义 Bienenstock-Cooper-Munro 框架，在模拟的前馈忆阻网络中展示了基于速率的方位选择性。这些发现为在忆阻器中模拟 Bienenstock-Cooper-Munro 学习规则提供了一种可行的方法，并支持使用忆阻网络进行时空编码和学习的应用。
