Slope-based stochastic resonance: how noise enables phasic neurons to encode slow signals.

Yan Gai, Brent Doiron, John Rinzel

Research output: Contribution to journalArticle

Abstract

Fundamental properties of phasic firing neurons are usually characterized in a noise-free condition. In the absence of noise, phasic neurons exhibit Class 3 excitability, which is a lack of repetitive firing to steady current injections. For time-varying inputs, phasic neurons are band-pass filters or slope detectors, because they do not respond to inputs containing exclusively low frequencies or shallow slopes. However, we show that in noisy conditions, response properties of phasic neuron models are distinctly altered. Noise enables a phasic model to encode low-frequency inputs that are outside of the response range of the associated deterministic model. Interestingly, this seemingly stochastic-resonance (SR) like effect differs significantly from the classical SR behavior of spiking systems in both the signal-to-noise ratio and the temporal response pattern. Instead of being most sensitive to the peak of a subthreshold signal, as is typical in a classical SR system, phasic models are most sensitive to the signal's rising and falling phases where the slopes are steep. This finding is consistent with the fact that there is not an absolute input threshold in terms of amplitude; rather, a response threshold is more properly defined as a stimulus slope/frequency. We call the encoding of low-frequency signals with noise by phasic models a slope-based SR, because noise can lower or diminish the slope threshold for ramp stimuli. We demonstrate here similar behaviors in three mechanistic models with Class 3 excitability in the presence of slow-varying noise and we suggest that the slope-based SR is a fundamental behavior associated with general phasic properties rather than with a particular biological mechanism.

Original languageEnglish (US)
JournalPLoS Computational Biology
Volume6
Issue number6
DOIs
StatePublished - 2010

Fingerprint

Stochastic Resonance
Neurons
Noise
Neuron
Slope
neurons
Low Frequency
Excitability
Architectural Accessibility
Signal-To-Noise Ratio
general property
Bandpass Filter
Bandpass filters
Neuron Model
mechanistic models
Deterministic Model
Signal to noise ratio
Model
signal-to-noise ratio
detectors

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Ecology
  • Molecular Biology
  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Computational Theory and Mathematics

Cite this

Slope-based stochastic resonance : how noise enables phasic neurons to encode slow signals. / Gai, Yan; Doiron, Brent; Rinzel, John.

In: PLoS Computational Biology, Vol. 6, No. 6, 2010.

Research output: Contribution to journalArticle

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