Design and implementation of artificial neuromorphic systems able to provide brain akin computation and/or bio-compatible interfacing ability are crucial for understanding the human brain’s complex functionality and unleashing brain-inspired computation’s full potential. To this end, the realization of energy-efficient, low-area, and bio-compatible artificial synapses, which sustain the signal transmission between neurons, is of particular interest for any large-scale neuromorphic system. Graphene is a prime candidate material with excellent electronic properties, atomic dimensions, and low-energy envelope perspectives, which was already proven effective for logic gates implementations. Furthermore, distinct from any other materials used in current artificial synapse implementations, graphene is biocompatible, which offers perspectives for neural interfaces. In view of this, we investigate the feasibility of graphene-based synapses to emulate various synaptic plasticity behaviors and look into their potential area and energy consumption for large-scale implementations. In this article, we propose a generic graphene-based synapse structure, which can emulate the fundamental synaptic functionalities, i.e., Spike-Timing-Dependent Plasticity (STDP) and Long-Term Plasticity . Additionally, the graphene synapse is programable by means of back-gate bias voltage and can exhibit both excitatory or inhibitory behavior. We investigate its capability to obtain different potentiation/depression time scale for STDP with identical synaptic weight change amplitude when the input spike duration varies. Our simulation results, for various synaptic plasticities, indicate that a maximum 30% synaptic weight change and potentiation/depression time scale range from [-1.5 ms, 1.1 ms to [-32.2 ms, 24.1 ms] are achievable. We further explore the effect of our proposal at the Spiking Neural Network (SNN) level by performing NEST-based simulations of a small SNN implemented with 5 leaky-integrate-and-fire neurons connected via graphene-based synapses. Our experiments indicate that the number of SNN firing events exhibits a strong connection with the synaptic plasticity type, and monotonously varies with respect to the input spike frequency. Moreover, for graphene-based Hebbian STDP and spike duration of 20ms we obtain an SNN behavior relatively similar with the one provided by the same SNN with biological STDP. The proposed graphene-based synapse requires a small area (max. 30 nm 2 ), operates at low voltage (200 mV), and can emulate various plasticity types, which makes it an outstanding candidate for implementing large-scale brain-inspired computation systems.

中文翻译：

---

具有可调可塑性的基于石墨烯的人工突触

能够提供类脑计算和/或生物兼容接口能力的人工神经形态系统的设计和实现对于理解人脑的复杂功能和释放类脑计算的全部潜力至关重要。为此，维持神经元之间的信号传输的节能、低面积和生物相容的人工突触的实现对于任何大规模的神经形态系统都特别感兴趣。石墨烯是一种主要的候选材料，具有出色的电子特性、原子尺寸和低能量包络透视，已被证明对逻辑门实现有效。此外，与当前人工突触实施中使用的任何其他材料不同，石墨烯具有生物相容性，它为神经接口提供了视角。鉴于此，我们研究了基于石墨烯的突触模拟各种突触可塑性行为的可行性，并研究了它们在大规模实施中的潜在面积和能耗。在本文中，我们提出了一种通用的基于石墨烯的突触结构，它可以模拟基本的突触功能，即尖峰时间相关可塑性 (STDP)和长期可塑性. 此外，石墨烯突触可以通过背栅偏置电压进行编程，并且可以表现出兴奋或抑制行为。我们研究了它在输入尖峰持续时间变化时获得具有相同突触权重变化幅度的 STDP 的不同增强/抑制时间尺度的能力。我们的模拟结果，对于各种突触可塑性，表明最大 30% 的突触重量变化和增强/抑制时间尺度范围从 [-1.5 ms，1.1 ms 到 [-32.2 ms，24.1 ms] 是可以实现的。我们进一步探讨了我们的提议在尖峰神经网络 (SNN)通过对一个小型 SNN 执行基于 NEST 的模拟，该 SNN 由 5 个通过基于石墨烯的突触连接的泄漏集成和激发神经元实现。我们的实验表明，SNN 触发事件的数量与突触可塑性类型有很强的联系，并且随着输入尖峰频率的变化而单调变化。此外，对于基于石墨烯的 Hebbian STDP 和 20 毫秒的尖峰持续时间，我们获得的 SNN 行为与具有生物 STDP 的相同 SNN 提供的行为相对相似。所提出的基于石墨烯的突触需要小面积（最大 30 nm2)，在低电压 (200 mV) 下运行，并且可以模拟各种可塑性类型，这使其成为实现大规模类脑计算系统的杰出候选者。