Noise in the nervous system. Faisal, A. A., Selen, L. P. J., & Wolpert, D. M. Nature Reviews Neuroscience, 9(4):292–303, Nature Publishing Group, April, 2008. ![Noise in the nervous system [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{faisalNoiseNervousSystem2008e,
title = {Noise in the nervous system},
volume = {9},
copyright = {2008 Springer Nature Limited},
issn = {1471-0048},
url = {https://www.nature.com/articles/nrn2258},
doi = {10.1038/nrn2258},
abstract = {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.},
language = {en},
number = {4},
urldate = {2024-04-29},
journal = {Nature Reviews Neuroscience},
publisher = {Nature Publishing Group},
author = {Faisal, A. Aldo and Selen, Luc P. J. and Wolpert, Daniel M.},
month = apr,
year = {2008},
keywords = {Animal Genetics and Genomics, Behavioral Sciences, Biological Techniques, Biomedicine, Neurobiology, Neurosciences, general},
pages = {292--303},
}
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