Institute of Modern Circuit and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China.
Chaos. 2023 Feb;33(2):023120. doi: 10.1063/5.0138363.
Neuromorphic computing provides unique computing and memory capabilities that could break the limitation of conventional von Neumann computing. Toward realizing neuromorphic computing, fabrication and synthetization of hardware elements and circuits to emulate biological neurons are crucial. Despite the striking progress in exploring neuron circuits, the existing circuits can only reproduce monophasic action potentials, and no studies report on circuits that could emulate biphasic action potentials, limiting the development of neuromorphic devices. Here, we present a simple third-order memristive circuit built with a classical symmetrical Chua Corsage Memristor (SCCM) to accurately emulate biological neurons and show that the circuit can reproduce monophasic action potentials, biphasic action potentials, and chaos. Applying the edge of chaos criterion, we calculate that the SCCM and the proposed circuit have the symmetrical edge of chaos domains with respect to the origin, which plays an important role in generating biphasic action potentials. Also, we draw a parameter classification map of the proposed circuit, showing the edge of chaos domain (EOCD), the locally active domain, and the locally passive domain. Near the calculated EOCD, the third-order circuit generates monophasic action potentials, biphasic action potentials, chaos, and ten types of symmetrical bi-directional neuromorphic phenomena by only tuning the input voltage, showing a resemblance to biological neurons. Finally, a physical SCCM circuit and some experimentally measured neuromorphic waveforms are exhibited. The experimental results agree with the numerical simulations, verifying that the proposed circuit is suitable as artificial neurons.
神经形态计算提供了独特的计算和存储能力,可以突破传统冯·诺依曼计算的限制。为了实现神经形态计算,制造和综合硬件元件和电路以模拟生物神经元至关重要。尽管在探索神经元电路方面取得了显著进展,但现有的电路只能复制单相动作电位,并且没有研究报告能够模拟双相动作电位的电路,这限制了神经形态器件的发展。在这里,我们提出了一个简单的三阶忆阻电路,该电路由经典对称蔡氏混沌忆阻器 (SCCM) 构建,可准确模拟生物神经元,并表明该电路可以复制单相动作电位、双相动作电位和混沌。应用混沌边缘准则,我们计算出 SCCM 和所提出的电路相对于原点具有对称的混沌边缘域,这对于产生双相动作电位起着重要作用。此外,我们绘制了所提出的电路的参数分类图,显示了混沌边缘域 (EOCD)、局部活动域和局部被动域。在计算出的 EOCD 附近,三阶电路通过仅调整输入电压就可以产生单相动作电位、双相动作电位、混沌和十种对称双向神经形态现象,类似于生物神经元。最后,展示了一个物理 SCCM 电路和一些实验测量的神经形态波形。实验结果与数值模拟一致,验证了所提出的电路适合作为人工神经元。