Adaptive synaptic adjustment mechanism to improve learning performances of spiking neural networks

摘要: Spiking Neural Networks (SNNs) are currently attracting researchers' attention due to their efficiencies in various tasks. Spike‐timing‐dependent plasticity (STDP) is an unsupervised learning process that utilizes bio‐plausibility based on the relative timing of pre/post‐synaptic spikes of neurons. Integrated with STDP, SNNs perform well consuming less energy. However, it is hard to ensure that synaptic weights always converge to values guaranteeing accurate prediction because STDP does not change synaptic weights with supervision. To address this limitation, researchers have proposed mechanisms for inducing STDP to converge synaptic weights on the proper values referring to current synaptic weights. Thus, if the current weights fail to describe proper synaptic connections, they cannot induce STDP to update synaptic weights properly. To solve this problem, we propose an adaptive mechanism that helps …