This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel
brain-inspired unsupervised local learning rule for the online implementation
of Hebb's plasticity mechanism on neuromorphic hardware. The proposed VDSP
learning rule updates the synaptic conductance on the spike of the postsynaptic
neuron only, which reduces by a factor of two the number of updates with
respect to standard spike-timing-dependent plasticity (STDP). This update is
dependent on the membrane potential of the presynaptic neuron, which is readily
available as part of neuron implementation and hence does not require
additional memory for storage. Moreover, the update is also regularized on
synaptic weight and prevents explosion or vanishing of weights on repeated
stimulation. Rigorous mathematical analysis is performed to draw an equivalence
between VDSP and STDP. To validate the system-level performance of VDSP, we
train a single-layer spiking neural network (SNN) for the recognition of
handwritten digits. We report 85.01 $ \pm $ 0.76% (Mean $ \pm $ S.D.) accuracy
for a network of 100 output neurons on the MNIST dataset. The performance
improves when scaling the network size (89.93 $ \pm $ 0.41% for 400 output
neurons, 90.56 $ \pm $ 0.27 for 500 neurons), which validates the applicability
of the proposed learning rule for spatial pattern recognition tasks. Future
work will consider more complicated tasks. Interestingly, the learning rule
better adapts than STDP to the frequency of input signal and does not require
hand-tuning of hyperparameters

本文提出了一种基于脑类的无监督局部学习规则 VDSP，旨在在神经形态硬件上在线实现 Hebb 氏可塑性机制。该规则能够有效降低权值上的爆炸或消失，并且证明了在手写数字识别任务方面，其具有明显的优越性。