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When response variability increases neural network robustness to synaptic noise.

Gleb Basalyga1, Emilio Salinas

  • 1Department of Neurobiology and Anatomy, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1010, USA. gbasalyg@wfubmc.edu

Neural Computation
|June 13, 2006
PubMed
Summary
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Neural response variability, often seen as noise, can actually improve brain network accuracy. This neuronal noise benefits performance by compensating for changing synaptic strengths, especially in networks that continuously learn.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Cortical sensory neurons exhibit significant trial-to-trial response variability.
  • This variability is typically considered detrimental noise, reducing neural circuit accuracy.

Purpose of the Study:

  • Investigate the potential benefits of response variability in neural networks.
  • Examine the interplay between neuronal response noise and synaptic noise.

Main Methods:

  • Analyzed the joint influence of response noise and synaptic noise on network accuracy.
  • Used analytical models and computer simulations across various network architectures.

Main Results:

  • Response noise can significantly improve network performance when synaptic noise is multiplicative.

Related Experiment Videos

  • Neuronal noise and multiplicative synaptic noise exhibit a compensatory interaction.
  • Networks trained with response noise show increased resistance to synaptic degradation.

    • Conclusions:

    • Neuronal response variability may play a beneficial dynamic role in learning networks.
    • The findings challenge the traditional view of neural variability as solely detrimental noise.