Collins J J, Chow C C, Imhoff T T
NeuroMuscular Research Center, Boston University, Massachusetts 02215, USA.
Nature. 1995 Jul 20;376(6537):236-8. doi: 10.1038/376236a0.
Stochastic resonance (SR) is a phenomenon wherein the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular, non-zero level of noise. SR has been proposed as a means for improving signal detection in a wide variety of systems, including superconducting quantum interference devices, and may be used in some natural systems such as sensory neurons. But for SR to be effective in a single-unit system (such as a sensory neuron or a single ion channel), the optimal intensity of the noise must be adjusted as the nature of the signal to be detected changes. This has been thought to impose a limitation on the practical and natural uses of SR. Here we show that the ability of a summing network of excitable units to detect a range of weak (sub-threshold) signals (either periodic or aperiodic) can be optimized by a fixed level of noise, irrespective of the nature of the input signal. We also show that this noise does not significantly degrade the ability of the network to detect suprathreshold signals. Thus, large nonlinear networks do not suffer from the limitations of SR in single units, and might be able to use a single noise level, such as that provided by the intrinsic noise of the individual components, to enhance the system's sensitivity to weak inputs. This suggests a functional role for neuronal noise in sensory systems.
随机共振（SR）是一种现象，即非线性系统对微弱周期性输入信号的响应通过特定的非零噪声水平得到优化。随机共振已被提出作为一种在包括超导量子干涉器件在内的各种系统中改善信号检测的手段，并且可能用于某些自然系统，如感觉神经元。但是，要使随机共振在单单元系统（如感觉神经元或单个离子通道）中有效，必须随着待检测信号的性质变化而调整噪声的最佳强度。人们一直认为这对随机共振的实际应用和自然应用构成了限制。在此我们表明，兴奋性单元的求和网络检测一系列微弱（阈下）信号（周期性或非周期性）的能力可通过固定水平的噪声得到优化，而与输入信号的性质无关。我们还表明，这种噪声不会显著降低网络检测阈上信号的能力。因此，大型非线性网络不会受到单单元随机共振局限性的影响，并且可能能够使用单一噪声水平，例如由各个组件的固有噪声提供的噪声水平，来提高系统对微弱输入的灵敏度。这表明了神经元噪声在感觉系统中的功能作用。
