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Stochastic resonance without tuning.

机译：随机共振，无需调整。

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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.
机译：随机共振（SR）是一种现象，其中非线性系统对微弱的周期性输入信号的响应通过存在特定的，非零电平的噪声而得到优化。 SR已经被提出作为改善包括超导量子干扰设备在内的各种系统中信号检测的一种手段，并且可以用于某些自然系统中，例如感觉神经元。但是，为了使SR在单单元系统（例如，感觉神经元或单个离子通道）中有效，必须随着要检测的信号性质的变化来调整噪声的最佳强度。已经认为这对SR的实际和自然使用施加了限制。在这里，我们显示了可激励单元求和网络检测一定范围的弱（亚阈值）信号（周期性或非周期性）的能力，可以通过固定的噪声水平来优化，而与输入信号的性质无关。我们还表明，这种噪声不会显着降低网络检测超阈值信号的能力。因此，大型非线性网络不会受到单个单位SR的限制，并且可能能够使用单个噪声级（例如各个组件的固有噪声所提供的噪声级）来增强系统对弱输入的灵敏度。这暗示了感觉系统中神经元噪声的功能性作用。

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