# Voltage-Dependent Synaptic Plasticity (VDSP): Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential

arXiv: https://arxiv.org/abs/2203.11022 · HTML: https://mlsanity.com/paper/2203.11022/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception](https://mlsanity.com/paper/1807.10936/) (2018)
- [Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware](https://mlsanity.com/paper/1812.01739/) (2018)
- [STDP-based spiking deep convolutional neural networks for object recognition](https://mlsanity.com/paper/1611.01421/) (2016)
- [Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System](https://mlsanity.com/paper/1604.05080/) (2016)
- [Verification and Design Methods for the BrainScaleS Neuromorphic Hardware System](https://mlsanity.com/paper/2003.11455/) (2020)

## Built on by (most cited first, 2 of 2)

- [Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16235/) (2023)
- [Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses](https://mlsanity.com/paper/2510.25787/) (2025)

## Abstract (arXiv)

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  ±  0.76% (Mean  ±  S.D.) accuracy for a network of 100 output neurons on the MNIST dataset. The performance improves when scaling the network size (89.93  ±  0.41% for 400 output neurons, 90.56  ±  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