These include crayfish mechanoreceptors 177, shark multimodal sensory cells 178, cricket cercal sensory neurons 179 and human muscle spindles 180. Since its first discovery in cat visual neurons 176, stochastic-resonance-type effects have been demonstrated in a range of sensory systems. For stochastic resonance to be useful, positive detection of a sub-threshold input must be more desirable than a failure to detect a supra-threshold input. For intermediate noise intensities, however, the noise allows the signal to reach the threshold but does not swamp it. For large noise levels, the response is dominated by the noise. At low noise levels, the sensory signal does not cause the system to cross the threshold and few signals are detected. For example, stochastic resonance is a process by which the ability of threshold-like systems to detect and transmit weak (periodic) signals can be enhanced by the presence of a certain level of noise 87, 175. Several strategies have been adopted to use noise in this fashion. Noise is not only a problem for neurons: it can also be a solution to other information-processing issues. Given the many levels and systems that are spanned, we cannot provide a comprehensive Review, but instead we pick out specific examples that reflect in a more general manner the constraints and limitations that noise sets in the CNS the benefits of noise are discussed in BOX 1. Finally, we discuss the strategies that the nervous system uses to counter, compensate for or account for noise in perception, decision making and motor behaviour. As the brain's purpose is to receive and process information and act in response to that information, we then examine how noise affects motor behaviour, considering the contribution of noise to variability at each level of the behavioural loop. In this Review, we begin by considering the nature, amount and effects of noise in the CNS. In recent years the extent to which noise is present and how noise shapes the structure and function of nervous systems have been studied. Noise permeates every level of the nervous system, from the perception of sensory signals to the generation of motor responses, and poses a fundamental problem for information processing 5, 6. Whereas previous reviews have focused on neuronal variability in general, we focus here on work directly relating to noise. The second source of variability is noise, which is defined in the Oxford English Dictionary as “random or irregular fluctuations or disturbances which are not part of a signal or which interfere with or obscure a signal or more generally any distortions or additions which interfere with the transfer of information”. The variability in the response will be exacerbated if the system's dynamics are highly sensitive to the initial conditions. For example, the initial state of the neural circuitry will vary at the start of each trial, leading to different neuronal and behavioural responses. The first source is the deterministic properties of the system. Trial-to-trial variability can arise from two distinct sources. Importantly, the term variability does not indicate that a particular mechanism has generated the variability, and does not suggest whether the variability is beneficial or detrimental. In this Review, we use the term variability to refer to changes in some measurable quantity, such as spike timing or movement duration. What are the sources of this variability? Here, a linguistic problem arises, as each field has developed its own interpretation of terms such as variability, fluctuation and noise. Such variability is also observed at the neuronal level 1 - 4. Variability in perception and action is observed even when external conditions, such as the sensory input or task goal, are kept as constant as possible. Variability is a prominent feature of behaviour.
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