The dependence of four firing statistics in neuronal excitable systems is studied as a function
of noise intensity and sinusoidal forcing period. For a range of biologically relevant …

We consider here the effect of temporal correlations on the transmission of spike trains in an
integrate-and-fire neuron. We find that if the noisy input spike train is variable enough, the …

Two recently suggested mechanisms for the neuronal encoding of sensory information
involving the effect of stochastic resonance with aperiodic time-varying inputs are …

We theoretically describe how weak signals may be efficiently transmitted throughout more
than one frequency range in noisy excitable media by a sort of stochastic multiresonance …

Periodically stimulated sensory neurons typically exhibit a kind of “statistical phase locking”
to the stimulus: they tend to fire at a preferred phase of the stimulus cycle, but not at every …

The paradigm of stochastic resonance (SR)—the idea that signal detection and transmission
may benefit from noise—has met with great interest in both physics and the neurosciences …

Input noise, defined as the root mean square of the fluctuations in the input, typically limits
the performance of any system in engineering or biology. We show that three different …

We investigated the resonance behavior in model neurons receiving a large number of
random synaptic inputs, whose distributed nature permits one to introduce correlations …

Numerical simulation of a classic integrate-and-fire neuron model, driven by a periodic spike
train and a Poisson noise train, demonstrates that stochastic resonance is a property that is …

The idea that neurons might use stochastic resonance (SR) to take advantage of random
signals has been extensively discussed in the literature. However, there are a few key …