Du Nan, Zhao Xianyue, Chen Ziang, Choubey Bhaskar, Di Ventra Massimiliano, Skorupa Ilona, Bürger Danilo, Schmidt Heidemarie
Department Nano Device Technology, Fraunhofer Institute for Electronic Nano Systems, Chemnitz, Germany.
Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology, Chemnitz, Germany.
Front Neurosci. 2021 Jul 14;15:660894. doi: 10.3389/fnins.2021.660894. eCollection 2021.
Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the complete functionality of biological synapses upon different neuronal activities in order to process big data flows in an efficient and cognitive manner while being robust against any noisy input. The memristive device has been proposed as a promising candidate for emulating artificial synapses due to their complex multilevel and dynamical plastic behaviors. In this work, we exploit ultrastable analog BiFeO (BFO)-based memristive devices for experimentally demonstrating that BFO artificial synapses support various long-term plastic functions, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), and spiking rate-dependent plasticity (SRDP). The study on the impact of electrical stimuli in terms of pulse width and amplitude on STDP behaviors shows that their learning windows possess a wide range of timescale configurability, which can be a function of applied waveform. Moreover, beyond SRDP, the systematical and comparative study on generalized frequency-dependent plasticity (FDP) is carried out, which reveals for the first time that the ratio modulation between pulse width and pulse interval time within one spike cycle can result in both synaptic potentiation and depression effect within the same firing frequency. The impact of intrinsic neuronal noise on the STDP function of a single BFO artificial synapse can be neglected because thermal noise is two orders of magnitude smaller than the writing voltage and because the cycle-to-cycle variation of the current-voltage characteristics of a single BFO artificial synapses is small. However, extrinsic voltage fluctuations, e.g., in neural networks, cause a noisy input into the artificial synapses of the neural network. Here, the impact of extrinsic neuronal noise on the STDP function of a single BFO artificial synapse is analyzed in order to understand the robustness of plastic behavior in memristive artificial synapses against extrinsic noisy input.
新兴的受大脑启发的神经形态计算范式需要能够在不同神经元活动时模拟生物突触完整功能的器件，以便以高效且具有认知能力的方式处理大数据流，同时对任何噪声输入具有鲁棒性。忆阻器件因其复杂的多级和动态可塑性行为，已被提议作为模拟人工突触的有前景的候选者。在这项工作中，我们利用基于超稳定模拟BiFeO（BFO）的忆阻器件进行实验，以证明BFO人工突触支持各种长期可塑性功能，即脉冲时间依赖可塑性（STDP）、循环次数依赖可塑性（CNDP）和发放率依赖可塑性（SRDP）。关于电刺激的脉冲宽度和幅度对STDP行为影响的研究表明，它们的学习窗口具有广泛的时间尺度可配置性，这可以是所施加波形的函数。此外，除了SRDP，还对广义频率依赖可塑性（FDP）进行了系统的比较研究，首次揭示了在一个尖峰周期内脉冲宽度与脉冲间隔时间之间的比率调制可在相同发放频率下导致突触增强和抑制效应。单个BFO人工突触的固有神经元噪声对STDP功能的影响可以忽略不计，这是因为热噪声比写入电压小两个数量级，并且单个BFO人工突触的电流 - 电压特性的逐周期变化很小。然而，外部电压波动，例如在神经网络中，会给神经网络的人工突触带来噪声输入。在此，分析了外部神经元噪声对单个BFO人工突触的STDP功能的影响，以了解忆阻人工突触中可塑性行为对外部噪声输入的鲁棒性。
