Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons
Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons
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DOI：
10.1088/1361-6463/aad361
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发表时间：
2018-08-30
影响因子：
3.4
通讯作者：
Spiga, S.
中科院分区：
文献类型：
--
作者：
Covi, E.;George, R.;Spiga, S.
生物学上合理的神经形态计算系统由于其低延迟、大规模并行信息处理能力和高能效而吸引了相当多的关注。为了实现这些功能，神经形态硅神经元电路需要与能够在线学习和存储突触权重的塑料突触电路集成。在此背景下，忆阻器件由于其非易失性、可扩展性以及与互补金属氧化物半导体制造工艺的兼容性而发挥关键作用。然而，神经忆阻系统仍然面临着实现高效学习协议的困难挑战。在这里，我们提出并证明了在硬件中的尖峰驱动的阈值为基础的学习规则，超越传统的尖峰定时依赖的可塑性机制，还考虑到神经元膜电位和它的发射率。我们演示的混合忆阻神经形态系统包括放置在两个硅神经元之间的基于氧化物的忆阻突触设备，该设备在神经形态芯片上实现，该芯片包括设计用于驱动忆阻元件的适当接口和基于尖峰的学习电路。我们展示了该系统如何能够实时仿真权重相关的突触后活动，并在忆阻突触水平上驱动突触权重更新，遵循尖峰驱动的学习规则。我们通过实验结果验证了这种基于尖峰的学习机制，并通过基本的学习实验量化了系统性能。
Biologically plausible neuromorphic computing systems are attracting considerable attention due to their low latency, massively parallel information processing abilities, and their high energy efficiency. To achieve these features neuromorphic silicon neuron circuits need to be integrated with plastic synapse circuits capable of on-line learning and storage of synaptic weights. Within this context, memristive devices play a key role thanks to their non-volatility, scalability, and compatibility with the complementary metal-oxide-semiconductor fabrication process. However, neuro-memristive systems are still facing difficult challenges for implementing efficient learning protocols. Here, we propose and demonstrate in hardware a spike-driven threshold-based learning rule which goes beyond conventional spike-timing dependent plasticity mechanisms, by also taking into account the neuron membrane potential and its firing rate. The mixed memristive-neuromorphic system we demonstrate comprises an oxide-based memristive synapse device placed between two silicon neurons implemented on a neuromorphic chip that comprises the proper interfacing and spike-based learning circuits designed to drive the memristive elements. We show how the system is able to emulate in real-time weight dependent post-synaptic activity and drive synaptic weight updates at the memristive synapse level following the spike-driven learning rule presented. We validate this spike-based learning mechanism with experimental results and quantify the system performance with basic learning experiments.