Stochastic phase-change neurons
Stochastic phase-change neurons
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DOI：
10.1038/nnano.2016.70
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发表时间：
2016-08-01
影响因子：
38.3
通讯作者：
Eleftheriou, Evangelos
中科院分区：
文献类型：
--
作者：
Tuma, Tomas;Pantazi, Angeliki;Eleftheriou, Evangelos
基于尖峰神经元群体的人工神经形态系统是理解人脑和构建拟神经计算系统不可或缺的工具。为了达到与生物系统中所见的面积和功率效率相当的面积和功率效率，已经探索了基于电离子和基于相变的忆阻器件作为突触的纳米级对应物。然而，到目前为止，神经元的可扩展实现的进展有限。在这里，我们表明，硫族化合物为基础的相变材料可用于创建一个人工神经元，其中膜电位表示的纳米级相变器件的相位配置。通过利用可逆的非晶到晶体相变的物理，我们表明，突触后电位的时间整合可以在纳秒的时间尺度上实现。此外，我们表明，这是固有的随机性，因为当神经元被重置时发生的原子结构的熔化淬火诱导的重新配置。我们演示了使用这些相变神经元，和他们的人口，在并行数据流中的时间相关性的检测和高带宽信号的子奈奎斯特表示。
Artificial neuromorphic systems based on populations of spiking neurons are an indispensable tool in understanding the human brain and in constructing neuromimetic computational systems. To reach areal and power efficiencies comparable to those seen in biological systems, electroionics-based and phase-change-based memristive devices have been explored as nanoscale counterparts of synapses. However, progress on scalable realizations of neurons has so far been limited. Here, we show that chalcogenide-based phase-change materials can be used to create an artificial neuron in which the membrane potential is represented by the phase configuration of the nanoscale phase-change device. By exploiting the physics of reversible amorphous-to-crystal phase transitions, we show that the temporal integration of postsynaptic potentials can be achieved on a nanosecond timescale. Moreover, we show that this is inherently stochastic because of the melt-quench-induced reconfiguration of the atomic structure occurring when the neuron is reset. We demonstrate the use of these phase-change neurons, and their populations, in the detection of temporal correlations in parallel data streams and in sub-Nyquist representation of high-bandwidth signals.