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Related Experiment Videos

Noise in neurons is message dependent.

G A Cecchi1, M Sigman, J M Alonso

  • 1Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10021, USA. guille@tlon.rockefeller.edu

Proceedings of the National Academy of Sciences of the United States of America
|May 3, 2000
PubMed
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Neuronal response variability, specifically action potential timing precision, depends on input signals. Error-correcting network structures can improve timing reliability beyond individual neuron capabilities.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Information Theory

Background:

  • Neuronal responses exhibit significant variability, impacting information processing.
  • Action potential timing precision is a key aspect of neural coding.
  • Current models often assume noise is independent of the input signal.

Purpose of the Study:

  • To investigate the relationship between input signals and neuronal firing variability.
  • To determine if action potential timing precision can be controlled by input.
  • To explore mechanisms for improving timing reliability in neural circuits.

Main Methods:

  • Analysis of common noisy spike generation models.
  • Theoretical considerations on input-dependent variability.

Related Experiment Videos

  • Illustration with data from the mammalian visual pathway.
  • Investigation of error-correcting network topologies.

    • Main Results:

    • Neuronal timing variability is a function of the input signal.
    • Variability can be precisely controlled by adjusting input.
    • The assumption of noise independence from the message is often violated.
    • Error-correcting topologies enhance timing reliability.

    Conclusions:

    • Action potential timing precision is not an intrinsic property but is shaped by inputs.
    • Neural coding is more complex than often assumed, with noise being signal-dependent.
    • Network architecture can overcome limitations of individual neuronal noise.