Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, Tennessee 37916, USA.
Department of Mechanical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
Nanoscale. 2019 Oct 28;11(40):18640-18652. doi: 10.1039/c9nr07288h. Epub 2019 Oct 4.
It is now known that mammalian brains leverage plasticity of both chemical and electrical synapses (ES) for collocating memory and processing. Unlike chemical synapses, ES join neurons via gap junction ion channels that permit fast, threshold-independent, and bidirectional ion transport. Like chemical synapses, ES exhibit activity-dependent plasticity, which modulates the ionic conductance between neurons and, thereby, enables adaptive synchronization of action potentials. Many types of adaptive computing devices that display discrete, threshold-dependent changes in conductance have been developed, yet far less effort has been devoted to emulating the continuously variable conductance and activity-dependent plasticity of ES. Here, we describe an artificial electrical synapse (AES) that exhibits voltage-dependent, analog changes in ionic conductance at biologically relevant voltages. AES plasticity is achieved at the nanoscale by linking dynamical geometrical changes of a host lipid bilayer to ion transport via gramicidin transmembrane ion channels. As a result, the AES uniquely mimics the composition, biophysical properties, bidirectional and threshold-independent ion transport, and plasticity of ES. Through experiments and modeling, we classify our AES as a volatile memristor, where the voltage-controlled conductance is governed by reversible changes in membrane geometry and gramicidin channel density. Simulations show that AES plasticity can adaptively synchronize Hodgkin-Huxley neurons. Finally, by modulating the molecular constituents of the AES, we show that the amplitude, direction, and speed of conductance changes can be tuned. This work motivates the development and integration of ES-inspired computing devices for achieving more capable neuromorphic hardware.
现在已知哺乳动物大脑利用化学和电突触（ES）的可塑性来配置记忆和处理。与化学突触不同，ES 通过间隙连接离子通道将神经元连接在一起，从而允许快速、无阈值和双向离子传输。与化学突触一样，ES 表现出活动依赖性可塑性，调节神经元之间的离子电导，从而实现动作电位的自适应同步。已经开发出许多类型的显示电导离散、阈值依赖变化的自适应计算设备，但在模拟 ES 的连续可变电导和活动依赖性可塑性方面的努力要少得多。在这里，我们描述了一种人工电突触（AES），它在生物相关电压下表现出离子电导的电压依赖性、模拟变化。AES 可塑性是通过将主脂质双层的动态几何变化与通过革兰氏菌素跨膜离子通道的离子运输联系起来，在纳米尺度上实现的。结果，AES 独特地模拟了 ES 的组成、生物物理特性、双向和无阈值的离子传输以及可塑性。通过实验和建模，我们将我们的 AES 分类为挥发性忆阻器，其中电压控制的电导由膜几何和革兰氏菌素通道密度的可逆变化来控制。模拟表明，AES 可塑性可以自适应地使 Hodgkin-Huxley 神经元同步。最后，通过调节 AES 的分子成分，我们表明可以调整电导变化的幅度、方向和速度。这项工作为实现更强大的神经形态硬件，推动了 ES 启发式计算设备的开发和集成。
