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 sp...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.展开更多
基金the Non Poisson Modeling of Neuron Synaptic Input and Critical Dynamics for Cortical Networks(Grant No.11772241).
文摘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.
