热动力学介导的随机磁隧道结内禀尖峰时间相关塑性
Intrinsic Spike Timing Dependent Plasticity in Stochastic Magnetic Tunnel Junctions Mediated by Heat Dynamics
摘要：
对高效认知计算的追求引起了类脑计算领域的广泛研究兴趣。类脑计算的目标是使用固态设备和电路来模拟生物神经元和突触的行为。在各种方法中，新兴的非易失性存储器技术对于模仿神经突触行为特别感兴趣。这些设备允许将生物神经元和突触的丰富动态映射到其固有的设备物理上。在这封信中，我们专注于尖峰定时依赖可塑性（STDP）的生物突触的行为，并提出了一种方法来实现的STDP行为的磁性隧道结（MTJ）器件。具体来说，我们利用时间依赖的热动力学和MTJ的响应的瞬时温度来模仿STDP行为。我们的模拟，基于宏观自旋模型的磁化动力学，表明，STDP可以通过施加简单的电压波形作为前和后神经元在MTJ器件上的尖峰响应，在随机磁性隧道结中模仿。
英文摘要：
The quest for highly efficient cognitive computing has led to extensive research interest for the field of neuromorphic computing. Neuromorphic computing aims to mimic the behavior of biological neurons and synapses using solid-state devices and circuits. Among various approaches, emerging non-volatile memory technologies are of special interest for mimicking neuro-synaptic behavior. These devices allow the mapping of the rich dynamics of biological neurons and synapses onto their intrinsic device physics. In this letter, we focus on Spike Timing Dependent Plasticity (STDP) behavior of biological synapses and propose a method to implement the STDP behavior in Magnetic Tunnel Junction (MTJ) devices. Specifically, we exploit the time-dependent heat dynamics and the response of an MTJ to the instantaneous temperature to imitate the STDP behavior. Our simulations, based on a macro-spin model for magnetization dynamics, show that, STDP can be imitated in stochastic magnetic tunnel junctions by applying simple voltage waveforms as the spiking response of pre- and post-neurons across an MTJ device.
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