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NJIT Mathematical Biology Seminar

Tuesday, March 2, 2010, 2:30pm
Cullimore Hall 611
New Jersey Institute of Technology

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Slope-based Stochastic Resonance: How Noise Enables Phasic Neuron Models to Encode Slow Signals

Yan (Felicia) Gai

Center for Neural Sciences, New York University

Phasic neurons are band-pass filters or slope detectors, because they do not respond to inputs containing only low frequencies or shallow slopes. Phasic neurons are widely present in sensory systems and are thought to encode fast changes in their inputs and neglect slow variations. However, we show that in noisy conditions, response properties of phasic neuron models are distinctly altered. Noise enables phasic models to encode low-frequency inputs that are outside of the response area of the models. Interestingly, this seemingly stochastic-resonance (SR) like response 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 it is in a classical SR system, phasic models are most sensitive to the signal^Rs rising and falling phases where the slopes are steep. This finding is consistent with the fact that there is not an a bsolute input threshold in terms of amplitude; rather, input threshold is more properly to be defined as a 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.