双层Hodgkin-Huxley神经元网络中的随机共振

随机共振是一种非零噪声优化系统响应的现象。运用信噪比的评价方式，研究单个Hodgkin-Huxley神经元及其所构建的双层神经元网络中的随机共振，来模拟生物感觉系统中检测微弱信号的随机共振现象。结果表明，单个神经元在阈值下存在噪声优化系统检测性能的随机共振现象，但是最优的噪声强度却随外部信号性质的改变而变化；双层神经元网络不但可以在固定的噪声强度上对一定幅度范围内的阈下信号进行优化检测，而且噪声的存在并没有降低网络对阈上信号的检测能力。

Stochastic resonance in a double layer Hodgkin-Huxley neuronal network

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.