Full-function Pavlov associative learning photonic neural networks based on SOA and DFB-SA
Dianzhuang Zheng,
Shuiying Xiang,
Xingxing Guo,
Yahui Zhang,
Xintao Zeng,
Xiaojun Zhu,
Yuechun Shi,
Xiangfei Chen,
Yue Hao
Affiliations
Dianzhuang Zheng
State Key Laboratory of Integrated Service Networks, Xidian University, Xian 710071, China
Shuiying Xiang
State Key Laboratory of Integrated Service Networks, Xidian University, Xian 710071, China
Xingxing Guo
State Key Laboratory of Integrated Service Networks, Xidian University, Xian 710071, China
Yahui Zhang
State Key Laboratory of Integrated Service Networks, Xidian University, Xian 710071, China
Xintao Zeng
State Key Laboratory of Integrated Service Networks, Xidian University, Xian 710071, China
Xiaojun Zhu
School of Information Science and Technology, Nantong University, Nantong, Jiangsu 226019, China.
Yuechun Shi
Yongjiang Laboratory, No. 1792 Cihai South Road, Ningbo 315202, China
Xiangfei Chen
Key Laboratory of Intelligent Optical Sensing and Manipulation, Ministry of Education, The National Laboratory of Solid State Micro Structures, The College of Engineering and Applied Sciences, Institute of Optical Communication Engineering, Nanjing University, Nanjing 210023, China
Yue Hao
State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xian 710071, China
Pavlovian associative learning, a form of classical conditioning, has significantly impacted the development of psychology and neuroscience. However, the realization of a prototypical photonic neural network (PNN) for full-function Pavlov associative learning, encompassing both photonic synapses and photonic neurons, has not been achieved to date. In this study, we propose and experimentally demonstrate the first InP-based full-function Pavlov associative learning PNN. The PNN utilizes semiconductor optical amplifiers (SOAs) as photonic synapses and the distributed feedback laser with a saturable absorber (DFB-SA) as the photonic spiking neuron. The connection weights between neurons in the PNN can be dynamically changed based on the fast, time-varying weighting properties of the SOA. The optical output of the SOA can be directly coupled into the DFB-SA laser for nonlinear computation without additional photoelectric conversion. The results indicate that the PNN can successfully perform brain-like computing functions such as associative learning, forgetting, and pattern recall. Furthermore, we analyze the performance of PNN in terms of speed, energy consumption, bandwidth, and cascadability. A computational model of the PNN is derived based on the distributed time-domain coupled traveling wave equations. The numerical results agree well with the experimental findings. The proposed full-function Pavlovian associative learning PNN is expected to play an important role in the development of the field of photonic brain-like neuromorphic computing.
