TY - JOUR

T1 - Exploring structural nonlinearity in binary polariton-based neuromorphic architectures

T2 - Использование структурной нелинейности в бинарных поляритон-базированных нейроморфных архитектурах

AU - Кавокин, Алексей Витальевич

AU - Седов, Евгений Сергеевич

PY - 2025/4/4

Y1 - 2025/4/4

N2 - 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.

AB - 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.

UR - https://www.mendeley.com/catalogue/08793383-af81-36ea-8983-59d5e55024f5/

U2 - 10.1103/physrevapplied.23.044013

DO - 10.1103/physrevapplied.23.044013

M3 - Article

VL - 23

JO - Physical Review Applied

JF - Physical Review Applied

SN - 2331-7019

IS - 4

M1 - 044013

ER -