Spiking Neural Networks (SNNs) are gaining widespread momentum in the field of neuromorphic computing. These network systems integrated with neurons and synapses provide computational efficiency by mimicking the human brain. It is desired to incorporate the biological neuronal dynamics, including complex spiking patterns which represent diverse brain activities within the neural networks. Earlier hardware realization of neurons was (1) area intensive because of large capacitors in the circuit design, (2) neuronal spiking patterns were demonstrated with clocked neurons at the device level. To achieve more realistic biological neuron spiking behavior, emerging memristive devices are considered promising alternatives. In this paper, we propose, PrMnO3(PMO) -RRAM device-based neuron. The voltage-controlled electrothermal timescales of the compact PMO RRAM device replace the electrical timescales of charging a large capacitor. The electrothermal timescale is used to implement an integration block with multiple voltage-controlled timescales coupled with a refractory block to generate biological neuronal dynamics. Here, first, a Verilog-A implementation of the thermal device model is demonstrated, which captures the current-temperature dynamics of the PMO device. Second, a driving circuitry is designed to mimic different spiking patterns of cortical neurons, including Intrinsic bursting (IB) and Chattering (CH). Third, a neuron circuit model is simulated, which includes the PMO RRAM device model and the driving circuitry to demonstrate the asynchronous neuron behavior. Finally, a hardware-software hybrid analysis is done in which the PMO RRAM device is experimentally characterized to mimic neuron spiking dynamics. The work presents a realizable and more biologically comparable hardware-efficient solution for large-scale SNNs.

中文翻译：

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利用 PrMnO3 RRAM 中的电热时间尺度来构建一个紧凑、无时钟的神经元，表现出生物尖峰模式

尖峰神经网络 (SNN) 在神经形态计算领域获得了广泛的发展势头。这些与神经元和突触集成的网络系统通过模仿人脑来提供计算效率。需要结合生物神经元动力学，包括代表神经网络内不同大脑活动的复杂尖峰模式。早期的神经元硬件实现是 (1) 由于电路设计中的大电容器而占用大量面积，(2) 神经元尖峰模式在设备级别用时钟神经元进行演示。为了实现更逼真的生物神经元尖峰行为，新兴的忆阻器件被认为是有前途的替代品。在本文中，我们提出了基于 PrMnO3(PMO) -RRAM 设备的神经元。紧凑型 PMO RRAM 器件的压控电热时间尺度取代了为大电容器充电的电气时间尺度。电热时间标度用于实现具有多个电压控制时间标度的积分块以及耐火块以生成生物神经元动力学。在这里，首先展示了热器件模型的 Verilog-A 实现，它捕获了 PMO 器件的电流-温度动态。其次，驱动电路旨在模拟皮层神经元的不同尖峰模式，包括内在爆发 (IB) 和颤振 (CH)。第三，模拟神经元电路模型，其中包括 PMO RRAM 器件模型和用于演示异步神经元行为的驱动电路。最后，进行了硬件-软件混合分析，其中 PMO RRAM 设备通过实验表征以模拟神经元尖峰动力学。这项工作为大规模 SNN 提供了一种可实现且更具生物学可比性的硬件效率解决方案。