Journal
JOURNAL OF LIGHTWAVE TECHNOLOGY
Volume 38, Issue 17, Pages 4691-4698Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JLT.2020.2995083
Keywords
Vertical cavity surface emitting lasers; Photonics; Neurons; Optical polarization; Dogs; Optical pulses; Biological neural networks; Photonic neural network; vertical-cavity surface-emitting lasers; associative learning and forgetting; spike-time-dependent plasticity; pattern recall
Funding
- National Natural Science Foundation of China [61974177, 61674119]
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In this article, we propose a photonic neural network (PNN) to emulate associative learning and forgetting for the first time. The PNN contains three photonic neurons based on 1550nm long-wavelength vertical-cavity surface-emitting lasers (VCSELs) subject to double polarized optical injection. A computational model of the PNN is derived based on the well-known spin-flip model. According to the learning rule named spike-time-dependent plasticity (STDP), the connection weight between neurons in the PNN is modified by self-learning. The simulation results show that, during the associative learning process, an association of feeding food and bell ringing is established within the Long-term synaptic potentiation window of the STDP; while, the association is forgotten within the Long-term depression window of the STDP in the forgetting process. Moreover, the effects of time interval between two pre-synaptic spikes on the speed of associative learning and forgetting are considered in detail. Finally, we further realize pattern recall based on the photonic associative learning, which shows promises for emerging applications of a large-scale energy-efficient PNN.
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