Todri-Sanial Aida, Carapezzi Stefania, Delacour Corentin, Abernot Madeleine, Gil Thierry, Corti Elisabetta, Karg Siegfried F, Nunez Juan, Jimenez Manuel, Avedillo Maria J, Linares-Barranco Bernabe
IEEE Trans Neural Netw Learn Syst. 2022 May;33(5):1996-2009. doi: 10.1109/TNNLS.2021.3107771. Epub 2022 May 2.
Brain-inspired computing employs devices and architectures that emulate biological functions for more adaptive and energy-efficient systems. Oscillatory neural networks (ONNs) are an alternative approach in emulating biological functions of the human brain and are suitable for solving large and complex associative problems. In this work, we investigate the dynamics of coupled oscillators to implement such ONNs. By harnessing the complex dynamics of coupled oscillatory systems, we forge a novel computation model-information is encoded in the phase of oscillations. Coupled interconnected oscillators can exhibit various behaviors due to the strength of the coupling. In this article, we present a novel method based on subharmonic injection locking (SHIL) for controlling the oscillatory states of coupled oscillators that allow them to lock in frequency with distinct phase differences. Circuit-level simulation results indicate SHIL effectiveness and its applicability to large-scale oscillatory networks for pattern recognition.
受脑启发的计算采用能够模拟生物功能的设备和架构，以构建更具适应性和能源效率的系统。振荡神经网络（ONNs）是一种模拟人类大脑生物功能的替代方法，适用于解决大型复杂的关联问题。在这项工作中，我们研究耦合振荡器的动力学以实现此类振荡神经网络。通过利用耦合振荡系统的复杂动力学，我们构建了一种新颖的计算模型——信息编码在振荡相位中。由于耦合强度的不同，耦合互连的振荡器可以表现出各种行为。在本文中，我们提出了一种基于亚谐波注入锁定（SHIL）的新颖方法，用于控制耦合振荡器的振荡状态，使它们能够以不同的相位差锁定频率。电路级仿真结果表明了亚谐波注入锁定的有效性及其在大规模振荡网络模式识别中的适用性。
