Trial-to-trial variability can result from both deterministic sources, such as complex dynamics or internal states, and randomness — that is, noise. This Review focuses on noise and its impact along the behavioural loop.
Sensory noise is noise in sensory signals and sensory receptors. It limits the amount of information that is available to other areas of the CNS.
Cellular noise is an underestimated contributor to neuronal variability. The stochastic nature of neuronal mechanisms becomes critical in the many small structures of the CNS.
Electrical noise in neurons, especially channel noise from voltage-gated ion channels, limits neuronal reliability and cell size, producing millisecond variability in action-potential initiation and propagation.
Synaptic noise results from the noisy biochemical processes that underlie synaptic transmission. Adding up these noise sources can account for the observed postsynaptic-response variability.
Noise build-up in neural networks can be contained by appropriate network layouts, homeostatic mechanisms and the threshold-like nature of neurons.
Motor noise results when neural signals are converted into forces. The architecture of motor neurons and their muscle fibres makes the conversion noisy. The brain organizes movements to minimize the effects of motor noise on movement variability.
Beneficial effects of noise include stochastic resonance in specific cases of sensory processing and forcing neural networks to be more robust and explore more states.
Behavioural variability, as observed in sensory estimation and movement tasks, appears to be mainly produced by noise.
The principle of averaging is one of two fundamental principles applied by the CNS to compensate for noise by summing over sources of redundant information.
The principle of prior knowledge is the other fundamental principle: it exploits the expected nature of signals and noise. The CNS often applies it in combination with averaging, such as in Bayesian cue combination in sensory processing.
Noise — random disturbances of signals — poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.
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We would like to thank H. Barlow, J. Niven and H. Robinson for comments on our manuscript. We acknowledge the financial support of the Wellcome Trust and the European project SENSOPAC (IST-2005-028056).
Stochastic simulator (synpase)
Random or unpredictable fluctuations and disturbances that are not part of a signal.
An action potential interpreted as a unitary pulse signal (that is, it either is or is not present), the timing of which determines its information content. Other properties of the action potential, such as its shape or depolarization levels, are ignored.
- Trial-to-trial variability
The differences between responses that are observed when the same experiment is repeated in the same specimen (for example, in the same neuron or in the same subject).
- Poisson process
A random process that generates binary (yes or no) events for which the probability of occurrence in any small time interval is low. The rate at which events occur completely determines the statistics of the events. Poisson processes have a Fano factor of 1.
- Fano factor
The ratio of the variance of a variable quantity to its mean.
- Stochastic process (random process)
A process that generates a series of random events.
- Positive feedback
Feedback that responds to a perturbation in the same direction as the perturbation, thereby amplifying its effect.
- Nodes of Ranvier
Regularly spaced gaps in the myelin sheath that surrounds a myelinated axon. They expose the axonal membrane to the extracellular fluid and contain large numbers of voltage-gated ion channels and thus enable conduction of the action potential.
- Patch-clamp technique
An electrophysiological method that allows the study of the flow of current through a very small patch of cell membrane, which can contain just a single ion channel.
- Signal-to-noise ratio
The ratio of how much power is contained in the signal over the power of the noise, often measured as the variance of the signal divided by the variance of the noise.
- Axon hillock
The anatomical part of a mammalian neuron that connects the cell body to the axon. Axon hillocks are the postulated primary site of action-potential initiation.
- Johnson noise (thermal noise, Johnson–Nyquist noise or Nyquist noise)
The electronic noise that is generated by the thermal agitation of the charge carriers (electrons and ions) inside an electrical conductor at equilibrium, which happens regardless of any applied voltage. Johnson noise is distinguished from shot noise, which consists of additional current fluctuations that occur when a voltage is applied to a resistance and a macroscopic current starts to flow.
- Shot noise
A type of noise that occurs when the finite number of signal particles, such as electrons or ions in an electrical circuit or photons arriving at a photoreceptor, is small enough to give rise to detectable statistical fluctuations in a measurement.
- Ephaptic coupling
The coupling of very close or touching neurons, mediated by the electrical fields the neurons generate during electrical activity.
- Coefficient of variation
(CV). The ratio of the standard deviation of a variable quantity to its mean.
- Release probability
The probability of a vesicle being released during a synaptic-transmission event.
The presence of superfluous or duplicate information in a message.
- White Gaussian noise process
A random process that generates a series of events, each Gaussian distributed. The mean and varience of the Gaussian completely determines the statistics of the series, and there is no temporal correlation between events.
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Faisal, A., Selen, L. & Wolpert, D. Noise in the nervous system. Nat Rev Neurosci 9, 292–303 (2008). https://doi.org/10.1038/nrn2258
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