@inproceedings{8e4a7060b1e44853b0c29169148c0f75,
title = "Dense Associative Memory in a nonlinear-optical Hopfield Neural Network",
abstract = "We present an experimental realization of a nonlinear-optical Hopfield neural network (NOHNN) designed for high-capacity dense associative memory (DAM). By exploiting the intrinsic nonlinearity of second-harmonic generation (SHG), we physically implement effective 4-body neuron interactions that significantly exceed the storage limits of traditional 2-body systems. The NOHNN demonstrates a minimum tenfold improvement in the storage of uncorrelated patterns and up to a fifty-fold enhancement for correlated patterns. Benchmarking using MNIST handwritten digits reveals a 5.5 times increase in storage capacity together with improved noise tolerance. These results highlight nonlinear photonics as a promising hardware platform for scalable associative memory and optimization.",
keywords = "Dense Associative Memory, Hopfield Network, Nonlinear Optics, Optical Neural Networks, Photonic Computing, Second-Harmonic Generation",
author = "Khalid Musa and Santosh Kumar and Michael Katidis and Yuping Huang",
note = "Publisher Copyright: {\textcopyright} 2026 SPIE. All rights reserved.; 2026 3rd Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences ; Conference date: 17-01-2026 Through 20-01-2026",
year = "2026",
month = mar,
day = "5",
doi = "10.1117/12.3080047",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Liang Gao and Guoan Zheng and Lee, \{Seung Ah\}",
booktitle = "Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences III",
}