STOCHASTIC RESONANCE WITHOUT TUNING

STOCHASTIC resonance(1-4) (SR) is a phenomenon wherein the response of a nonlinear system to a weak periodic input signal is optimized by th e presence of a particular, non-zero level of noises(5-7). SR has been proposed as a means for improving signal detection in a wide variety of systems, including superconducting quantum interference devices(8), and may be used in some natural systems such as sensory neurons(9-15) . But for SR to be effective in a single-unit system (such as a sensor y 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(15 ). This has been thought to impose a limitation on the practical and n atural 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 noi se, irrespective of the nature of the input signal. We also show that this noise does not significantly degrade the ability of the network t o 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 no ise of the individual components, to enhance the system's sensitivity to weak inputs. This suggests a functional role for neuronal noise(14, 16-18) in sensory systems.