  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
