This study investigates the performance of a binarized neuromorphic network leveraging polariton dyads, optically excited pairs of interfering polariton condensates within a microcavity to function as binary logic gate neurons. Employing numerical simulations, we explore various neuron configurations, both linear (NAND, NOR) and nonlinear (XNOR), to assess their effectiveness in image classification tasks. We demonstrate that structural nonlinearity, derived from the network's layout, plays a crucial role in facilitating complex computational tasks, effectively reducing the reliance on the inherent nonlinearity of individual neurons. Our findings suggest that the network's configuration and the interaction among its elements can emulate the benefits of nonlinearity, thus potentially simplifying the design and manufacturing of neuromorphic systems and enhancing their scalability. This shift in focus from individual neuron properties to network architecture could lead to significant advancements in the efficiency and applicability of neuromorphic computing.

翻译：本研究探讨了一种利用极化子二元体（微腔中光学激发的干涉极化子凝聚对）作为二元逻辑门神经元的二值化神经形态网络的性能。通过数值模拟，我们探索了多种神经元配置，包括线性（NAND、NOR）和非线性（XNOR）结构，以评估其在图像分类任务中的有效性。我们证明，源自网络布局的结构非线性在促进复杂计算任务方面起着关键作用，有效降低了对单个神经元固有非线性的依赖。我们的研究结果表明，网络的配置及其元素间的相互作用可以模拟非线性的优势，从而可能简化神经形态系统的设计与制造，并增强其可扩展性。这种从单个神经元特性到网络架构的焦点转移，可能会在神经形态计算的效率和适用性方面带来显著进步。