Abstract

The neurobiological learning algorithm, spike-timing-dependent plasticity (STDP), is demonstrated in a simple photonic system using the cooperative nonlinear effects of cross gain modulation and nonlinear polarization rotation, and supervised and unsupervised learning using photonic neuron principles are examined. An STDP-based supervised learning scheme is presented which is capable of mimicking a desirable spike pattern through learning and adaptation. Furthermore, unsupervised learning is illustrated by a principal component analysis system operating under similar learning rules. Finally, a photonic-distributed processing network capable of STDP-based unsupervised learning is theoretically explored.

© 2015 IEEE

Optical spike-timing-dependent plasticity with weight-dependent learning window and reward modulation

Quansheng Ren, Yaolin Zhang, Rui Wang, and Jianye Zhao
Opt. Express 23(19) 25247-25258 (2015)

Pulse lead/lag timing detection for adaptive feedback and control based on optical spike-timing-dependent plasticity

Mable P. Fok, Yue Tian, David Rosenbluth, and Paul R. Prucnal
Opt. Lett. 38(4) 419-421 (2013)

Photonic implementation of a neuronal algorithm applicable towards angle of arrival detection and localization

Ryan Toole and Mable P. Fok
Opt. Express 23(12) 16133-16141 (2015)

Delay-weight plasticity-based supervised learning in optical spiking neural networks

Yanan Han, Shuiying Xiang, Zhenxing Ren, Chentao Fu, Aijun Wen, and Yue Hao
Photon. Res. 9(4) B119-B127 (2021)

BP-based supervised learning algorithm for multilayer photonic spiking neural network and hardware implementation

Yahui Zhang, Shuiying Xiang, Yanan Han, Xingxing Guo, Wu Zhang, Qinggui Tan, Genquan Han, and Yue Hao
Opt. Express 31(10) 16549-16559 (2023)