Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

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

Gai, Y and Doiron, B and Rinzel, J (2010) Slope-based stochastic resonance: How noise enables phasic neurons to encode slow signals. PLoS Computational Biology, 6 (6). 1 - 22. ISSN 1553-734X

Published Version
Available under License : See the attached license file.

Download (1MB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)


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. © 2010 Gai et al.


Social Networking:
Share |


Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Gai, Y
Doiron, Bbdoiron@pitt.eduBDOIRON
Rinzel, J
ContributionContributors NameEmailPitt UsernameORCID
Date: 1 June 2010
Date Type: Publication
Journal or Publication Title: PLoS Computational Biology
Volume: 6
Number: 6
Page Range: 1 - 22
DOI or Unique Handle: 10.1371/journal.pcbi.1000825
Schools and Programs: Dietrich School of Arts and Sciences > Mathematics
Refereed: Yes
ISSN: 1553-734X
MeSH Headings: Action Potentials--physiology; Age Factors; Animals; Brain Stem--cytology; Brain Stem--physiology; Gerbillinae; Microscopy, Video; Models, Neurological; Neurons--physiology; Stochastic Processes
Other ID: NLM PMC2891698
PubMed Central ID: PMC2891698
PubMed ID: 20585612
Date Deposited: 03 Aug 2012 21:02
Last Modified: 02 Feb 2019 15:59


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item