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Voltage-dependent synaptic plasticity: Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane

Nikhil Garg1,2,3, Ismael Balafrej1,2,4, Terrence C Stewart5

  • 1Institut Interdisciplinaire d'Innovation Technologique (3IT), Université de Sherbrooke, Sherbrooke, QC, Canada.

Frontiers in Neuroscience
|November 7, 2022
PubMed
Summary

A new learning rule, voltage-dependent-synaptic plasticity (VDSP), enables efficient Hebbian learning on neuromorphic hardware. VDSP reduces updates and adapts to input frequency, outperforming standard methods in handwritten digit recognition.

Keywords:
Hebbian plasticitySTDPmodified national institute of standards and technology database (MNIST)spiking neural networkssynaptic plasticityunsupervised learning

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Area of Science:

  • Neuromorphic Engineering
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Spiking neural networks (SNNs) are bio-inspired computational models.
  • Implementing efficient learning rules on neuromorphic hardware is crucial for SNNs.
  • Traditional synaptic plasticity rules like STDP have limitations in hardware implementation.

Purpose of the Study:

  • To introduce a novel unsupervised, local learning rule called voltage-dependent-synaptic plasticity (VDSP).
  • To enable online implementation of Hebb's plasticity mechanism on neuromorphic hardware.
  • To reduce computational overhead and improve adaptability compared to existing methods.

Main Methods:

  • Developed VDSP, a learning rule updating synaptic conductance based on postsynaptic neuron spikes and presynaptic neuron membrane potential.
  • Performed mathematical analysis to establish equivalence between VDSP and STDP.
  • Trained a single-layer SNN using VDSP for handwritten digit recognition on the MNIST dataset.

Main Results:

  • VDSP reduces synaptic updates by half compared to STDP.
  • Achieved 85.01% accuracy on MNIST with 100 neurons, improving to 90.56% with 500 neurons.
  • VDSP demonstrated better adaptation to input signal frequency and robustness against hyperparameter tuning.

Conclusions:

  • VDSP is a viable and efficient learning rule for SNNs on neuromorphic hardware.
  • The proposed rule shows strong performance in spatial pattern recognition tasks.
  • VDSP offers advantages in terms of computational efficiency and adaptability over STDP.