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Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Coherence depression in stochastic excitable systems with two-frequency forcing.

Na Yu1, André Longtin

  • 1Department of Physics, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada. na.yu@uottawa.ca

Chaos (Woodbury, N.Y.)
|January 10, 2012
PubMed
Summary
This summary is machine-generated.

The leaky integrate-and-fire with dynamic threshold model better correlates neural responses to stimuli, but resonance can decrease coherence. Noise impacts information transmission in adaptive neuron models.

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Area of Science:

  • Computational Neuroscience
  • Neuronal Dynamics
  • Information Theory

Background:

  • Neuron models like the leaky integrate-and-fire (LIF) are crucial for understanding neural computation.
  • Dynamic thresholds in neuron models (LIFDT) introduce memory, potentially improving signal processing.
  • Naturalistic stimuli, including noise and amplitude modulation, are essential for realistic neural response analysis.

Purpose of the Study:

  • To compare the response of LIF and LIFDT neuron models to complex stimuli with noise.
  • To investigate how noise and resonance affect information transmission in these models.
  • To analyze the correlation between stimulus modulation and neural spike trains.

Main Methods:

  • Simulating two neuron models: leaky integrate-and-fire (LIF) and leaky integrate-and-fire with dynamic threshold (LIFDT).
  • Applying stimuli composed of two incommensurate sinusoidal drives, amplitude modulation noise, and additive noise.
  • Utilizing spectral and coherence analysis to quantify response fidelity and information transmission.

Main Results:

  • The LIFDT model showed better correlation between stimulus modulation and spike trains, even with noise.
  • Resonance-induced synchrony, near the neuron's firing rate, reduced coherence in the LIFDT model.
  • Modulation noise decreased linear spectral coherence between spikes and the stimulus/envelope, correlated with envelope fluctuation variability.

Conclusions:

  • The LIFDT model exhibits enhanced encoding of modulated stimuli compared to the standard LIF model.
  • Neuronal adaptation can lead to information transmission loss when encoding beats near the intrinsic firing rate.
  • Understanding these dynamics is key for deciphering neural coding under realistic, noisy conditions.