Signal-to-noise ratio gain of an adaptive neuron model with Gamma renewal synaptic input

We take an adaptive leaky integrate-and-fire neuron model to explore the effect of non-Poisson neurotransmitter on stochastic resonance and its signal-to-noise ratio (SNR) gain. Event triggered algorithm is adopted to speed up the simulating process. It is revealed that both the output SNR and the SNR gain can be monotonically improved when increasing the shape parameter for Gamma distribution. Particularly, for large signal coupling strength, the 1:1 stochastic phase locking induced by Gamma noise is responsible for the frequency matching stochastic resonance, and the output signal-to-noise ratio can surpass the input signal-to-noise ratio, which is significantly different with Poisson case, while for extremely weak signal coupling strength, the SNR gain peak, which is far larger than unity, is due to noise induced resonance. The observations are meaningful in understanding the neural processing mechanisms from a more realistic viewpoint of synaptic modeling.