Autonomous stochastic resonance in bursting neurons

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Abstract
Noise-induced firing is studied in two major classes of bursting neuron models in the absence of periodic input. In the biologically relevant subthreshold regime where no deterministic firing occurs, additive noise induces spiking limit cycles. This noise makes the output firing patterns sensitive to the characteristics of autonomous subthreshold oscillations, which can change in response to various physicochemical stimuli. The nonmonotonic behavior with increasing noise of the phase locking between spikes and subthreshold oscillations, measured using spectral signal-to-noise ratios and line shape characteristics, are a manifestation of autonomous stochastic resonance in these systems. The type of bifurcation giving rise to bursting activity determines the behavior with noise of the mean firing frequency, interspike interval histogram, spike train power spectrum, and phase locking. In particular, it is shown that a saddle-node bifurcation is not required to see stochastic resonance (SR) without periodic input when there exists a stable deterministic subthreshold oscillation. This paper also studies SR in a detailed ionic neuron model, an approach that leads to tests of hypotheses regarding the nature of noise in real neurons.