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Related Experiment Videos

What can a neuron learn with spike-timing-dependent plasticity?

Robert Legenstein1, Christian Naeger, Wolfgang Maass

  • 1Institute for Theoretical Computer Science, Technische Universitaet Graz, A-8010 Graz, Austria. legi@igi.tugraz.at

Neural Computation
|September 15, 2005
PubMed
Summary

Spiking neurons can learn transformations via spike-timing-dependent plasticity (STDP) with teacher forcing, but convergence is not guaranteed for all inputs. Average-case convergence is proven for Poisson inputs, extending to realistic models and synapse dynamics.

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Synaptic Plasticity

Background:

  • Spiking neurons are versatile computational units capable of diverse transformations through synaptic parameter adjustments.
  • Spike-timing-dependent plasticity (STDP) is a biologically plausible mechanism for synaptic modification.
  • Supervised learning paradigms, like teacher forcing, are used to train neural networks.

Purpose of the Study:

  • To investigate the extent to which spiking neurons with dynamic synapses can learn specific transformations using STDP under a supervised learning (teacher forcing) paradigm.
  • To compare the convergence properties of STDP with teacher forcing to the perceptron convergence theorem.

Main Methods:

  • Theoretical analysis of STDP convergence for spiking neurons with dynamic synapses under teacher forcing.

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  • Mathematical proofs for average-case convergence using uncorrelated and correlated Poisson input spike trains.
  • Extensive computer simulations using realistic neuron and synapse models and diverse input distributions.

    • Main Results:

    • Unlike the perceptron convergence theorem, STDP with teacher forcing lacks theoretical convergence guarantees for arbitrary input spike patterns.
    • Average-case convergence is proven for STDP with teacher forcing for Poisson inputs, with conditions analogous to linear separability applied to the input correlation matrix.
    • Simulations confirm theoretical predictions, demonstrating convergence for realistic models and synapse dynamics, including modulation of release probability.

    Conclusions:

    • STDP with teacher forcing offers a viable learning mechanism for spiking neurons, particularly under average-case conditions with specific input statistics.
    • The learning capabilities extend to biologically realistic scenarios, including dynamic synapses and varied input distributions.
    • STDP's role in modulating synaptic efficacy, beyond simple weight changes, is highlighted as crucial for learning.