Short-term forgetting at ultrafast timescales emulated by the depression function of a VCSOA-based photonic synapse
Chaotao He, Pu Ou, Qiupin Wang, Maorong Zhao, Ziyi Kang, Liang Peng, Junqi Liu, Dan Lu, Zhengmao Wu, Guangqiong XiaForgetting, an active optimization mechanism in the human brain, is governed by synaptic plasticity. This insight has motivated the development of artificial synapses for hardware-level emulation of forgetting. Among them, photonic synapses have drawn considerable attention owing to their ultrafast response and low crosstalk. However, short-term forgetting (STF) at ultrafast timescales remains unexplored in photonic synapses. Here, the STF at ultrafast timescales is demonstrated using a photonic synapse based on a vertical-cavity semiconductor optical amplifier (VCSOA). First, STF is realized by replicating bio-realistic paired-spike depression in a VCSOA under a paired optical spike injection, with dynamic control achieved by adjusting injection power and bias current. Subsequently, stimulating the VCSOA with multiple optical spikes produces multiple-spike depression, which can also simulate the STF at ultrafast timescales. To approximate a realistic STF scenario, a letter “T” pattern encoded by different spike numbers and time intervals is injected into the VCSOA-based photonic synapse, and a gradual forgetting trend similar to that of the human brain is clearly observed. Remarkably, all bio-inspired STF occur at nanosecond timescales, providing a tangible artificial synapse for photonic neuromorphic computing at nanosecond timescales.