Arbitrary Spike Time Dependent Plasticity (STDP) in Memristor by Analog Waveform Engineering
Arbitrary Spike Time Dependent Plasticity (STDP) in Memristor by Analog Waveform Engineering
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DOI：
10.1109/led.2017.2696023
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发表时间：
2017-04
影响因子：
4.9
通讯作者：
N. Panwar;B. Rajendran;U. Ganguly
中科院分区：
文献类型：
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作者：
N. Panwar;B. Rajendran;U. Ganguly
在文献中，各种基于脉冲的编程方案已被用于模拟在生物突触中观察到的典型的基于尖峰时间依赖可塑性（STDP）的学习规则。在本文中，我们展示了通过使用受神经元动作电位启发的模拟编程波形来产生任意STDP行为的能力。首先，我们提出一种简单的算法来生成任意形式的STDP。其次，我们基于W/Pr₀.₇Ca₀.₃MnO₃/Pt忆阻器展示了STDP的一系列尖峰相关时间尺度的可行性，例如从生物的（约100毫秒）到加速的（约20微秒）。第三，我们通过实验展示了几种形式的STDP行为，其中神经元前和神经元后的波形在时间上是随机间隔的，类似于操作条件。STDP形状与波形吻合良好。因此，我们表明人工突触可以实现生物中所观察到的丰富性以及一系列从生物兼容到加速神经网络应用的STDP时间尺度。
In the literature, various pulse-based programming schemes have been used to mimic typical spike time-dependent plasticity (STDP)-based learning rule observed in biological synapses. In this letter, we demonstrate the capability to generated arbitrary STDP behaviors by using analog programming waveforms inspired by neuronal action potential. First, we propose a simple algorithm to generate any arbitrary form of STDP. Second, we show the feasibility of a range of spike correlation time scales for STDP, e.g., biological (~100 ms) to accelerated ( $\sim 20\mu {s})$ , based on W/Pr0.7Ca0.3MnO3/Pt based memristor. Third, we experimentally demonstrate several forms of STDP behaviors, where the pre- and post-neuronal waveforms are randomly spaced in time, akin to operational conditions. STDP shape corresponds well to waveforms. Thus, we show that artificial synapses can achieve the richness observed in biology as well as a range of STDP timescales for biologically compatible to accelerated neural network applications.