Stochastic resonance in a double layer Hodgkin-Huxley neuronal network
双层Hodgkin-Huxley神经元网络中的随机共振

Stochastic resonance is a phenomenon, wherein the response of a nonlinear system to a weak periodic signal is enhanced at a nonzero level of noise. In order to study the ability of the network in weak signal detection, Hodgkin-Huxley neuronal model was adopted for constructing a double layer neuronal network. Stochastic resonance in a single neuron was characterized with signal-to-noise ratio for showing the positive role of noise in subthreshold signal detection. But the optimal level of noise for stochastic resonance in a single neuron must be adjusted as the nature of the external signal changes. This has been thought to impose a limitation on the use of stochastic resonance in weak signal detection. The double layer neuronal network was investigated and the characteristics of stochastic resonance was observed. Results indicate that this network has the ability of detecting the subthreshold signal varying in a range of amplitude at a fixed noise level and the noise does not degrade the ability of the network to detect suprathreshold signals. Thus, the double layer neuronal network does not suffer from the limitation of stochastic resonance in a single neuron. This suggests a positive role of noise in sensory systems.