Department of Physics, California Institute of Technology, 1200 E. California Blvd, Pasadena, CA, 91125, USA.
Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, MA, 02215, USA.
Sci Rep. 2017 Mar 24;7(1):411. doi: 10.1038/s41598-017-00442-y.
Towards practical realization of brain-inspired computing in a scalable physical system, we investigate a network of coupled micromechanical oscillators. We numerically simulate this array of all-to-all coupled nonlinear oscillators in the presence of stochasticity and demonstrate its ability to synchronize and store information in the relative phase differences at synchronization. Sensitivity of behavior to coupling strength, frequency distribution, nonlinearity strength, and noise amplitude is investigated. Our results demonstrate that neurocomputing in a physically realistic network of micromechanical oscillators with silicon-based fabrication process can be robust against noise sources and fabrication process variations. This opens up tantalizing prospects for hardware realization of a low-power brain-inspired computing architecture that captures complexity on a scalable manufacturing platform.
为了在可扩展的物理系统中实现脑启发式计算的实际应用，我们研究了一个耦合微机械振荡器网络。我们在存在随机因素的情况下对这个全互联耦合非线性振荡器阵列进行了数值模拟，并展示了它在同步时通过相对相位差来同步和存储信息的能力。我们还研究了行为对耦合强度、频率分布、非线性强度和噪声幅度的敏感性。研究结果表明，使用基于硅的制造工艺的物理现实网络中的微机械振荡器进行神经计算可以在一定程度上抵抗噪声源和制造工艺变化的影响。这为在可扩展制造平台上实现低功耗、模拟大脑的计算架构提供了诱人的前景，该架构可以捕捉到复杂性。
