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Noise-induced divisive gain control in neuron models.

André Longtin1, Brent Doiron, Adi R Bulsara

  • 1Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, Ont., Canada K1N 6N5. alongtin@physics.uottawa.ca

Bio Systems
|December 3, 2002
PubMed
Summary

Noise can decrease a neuron's firing rate response by modulating inhibitory input. This study confirms noise-induced divisive gain control across various neuronal models, impacting neural computation.

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Area of Science:

  • Computational neuroscience
  • Neural modeling
  • Systems neuroscience

Background:

  • Shunting inhibition is a key mechanism for neuronal gain control.
  • Previous studies suggested noise can influence neuronal excitability.
  • A computational study indicated noise could decrease the slope of frequency-versus-input (f-I) characteristics.

Purpose of the Study:

  • To investigate noise-induced divisive gain control across different neuronal models.
  • To confirm the effect of increased inhibitory noise on neuronal f-I curves.
  • To explore the physiological constraints and model dependencies of this phenomenon.

Main Methods:

  • Simulations using the leaky integrate-and-fire (LIF) model with additive and multiplicative noise.
  • Analysis of the one-dimensional theta-neuron model.

Related Experiment Videos

  • Modeling a large-scale pyramidal cell from the electrosensory lateral line lobe with Poisson-distributed inputs.
  • Comparison of divisive effects across various models.

    • Main Results:

    • Noise-induced divisive gain control was observed in the standard LIF model with additive Gaussian white noise.
    • The effect was replicated in the LIF model with multiplicative noise on inhibitory conductance.
    • Noisy scaling of input currents and divisive inhibition were demonstrated in the theta-neuron and compartmental models.
    • The phenomenon was confirmed even when both excitatory and inhibitory inputs had Poisson statistics.

    Conclusions:

    • Noise-induced divisive gain control is a robust effect observable across diverse neuronal models.
    • The variation in noise intensity with input is dependent on physiological context and model type.
    • This mechanism offers a novel way to regulate neuronal responses and information processing.