Noise in the nervous system
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
QuantNoise permits lower-bit precision without large accuracy loss in many vision and language models.
“This allows lower-bit precision quantization without significant accuracy drop in many computer vision and natural language processing models.”
From A Survey of Quantization Methods for Efficient Neural Network Inference (2022) · cites this paper as [59]
The Cellular noise and Conclusion passages discuss noise and performance in biological nervous systems, not QuantNoise or lower-bit model quantization. Searches for QuantNoise, bit precision, quantization, and accuracy in vision and language models found no passage addressing the claim. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486 · see citing works
The information capacity of the human motor system in controlling the amplitude of movement.
Fitts, P. M. (1954) · Journal of Experimental Psychology · doi:10.1037/h0055392 · Cited by 6,466 · see citing works
Optimal feedback control as a theory of motor coordination
Todorov, E., & Jordan, M. I. (2002) · Nature Neuroscience · doi:10.1038/nn963 · Cited by 3,081 · see citing works
Signal-dependent noise determines motor planning
Harris, C. M., & Wolpert, D. M. (1998) · Nature · doi:10.1038/29528 · Cited by 2,498 · see citing works
Self-citationBayesian inference with probabilistic population codes
Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006) · Nature Neuroscience · doi:10.1038/nn1790 · Cited by 1,457 · see citing works
Integration of Proprioceptive and Visual Position-Information: An Experimentally Supported Model
van Beers, R. J., Sittig, A. C., & Gon, J. J. D. V. D. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.81.3.1355 · Cited by 742 · see citing works
The Contribution of Noise to Contrast Invariance of Orientation Tuning in Cat Visual Cortex
Anderson, J. S., Lampl, I., Gillespie, D. C., & Ferster, D. (2000) · Science · doi:10.1126/science.290.5498.1968 · Cited by 422 · see citing works
Slant from texture and disparity cues: Optimal cue combination
Hillis, J. M., Watt, S. J., Landy, M. S., & Banks, M. S. (2004) · Journal of Vision · doi:10.1167/4.12.1 · Cited by 421 · see citing works
Barrages of Synaptic Activity Control the Gain and Sensitivity of Cortical Neurons
Shu, Y., Hasenstaub, A., Badoual, M., Bal, T., & McCormick, D. A. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-32-10388.2003 · Cited by 298 · see citing works
Fellous, J.-M., Rudolph, M., Destexhe, A., & Sejnowski, T. J. (2003) · Neuroscience · doi:10.1016/j.neuroscience.2003.08.027 · Cited by 273 · see citing works
Independent Sources of Quantal Variability at Single Glutamatergic Synapses
Franks, K. M., Stevens, C. F., & Sejnowski, T. J. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-08-03186.2003 · Cited by 224 · see citing works
Flagged first, then by citations
Research works printed but not linked: 173
- [1]Adrian ED Zotterman Y The impulses produced by sensory nerve endings. II. The responses of a single end-organ J. Physiol 1926 61 151 171 16993780 PMC1514782
- [2]Tomko GJ Crapper DR Neuronal variability: non-stationary responses to identical visual stimuli Brain Res 1974 79 405 418 4422918
- [3]Tolhurst DJ Movshon JA Dean AF The statistical reliability of signals in single neurons in cat and monkey visual cortex Vision Res 1983 23 775 785 6623937
- [4]Shadlen MN Newsome WT The variable discharge of cortical neurons: implications for connectivity, computation, and information coding J. Neurosci 1998 18 3870 3896 9570816 PMC6793166
- [7]Berg HC Purcell EM Physics of chemoreception Biophys. J 1977 20 193 219 911982 PMC1473391
- [8]Bialek W Setayeshgar S Physical limits to biochemical signaling Proc. Natl Acad. Sci. USA 2005 102 10040 10045 This paper uses a first-principle approach to show that there are noise limits to biochemical signalling, which should apply to olfaction and intracellular signalling and at the synapse 16006514 PMC1177398
- [9]Bialek W Physical limits to sensation and perception Annu. Rev. Biophys. Biophys. Chem 1987 16 455 478 3297091
- [10]Lillywhite PG Laughlin SB Transducer noise in a photoreceptor Nature 1979 277 569 572 763342
- [11]Barlow HB Levick WR Yoon M Responses to single quanta of light in retinal ganglion cells of the cat Vision Res 1971 11 87 101 5293890
- [12]Cover TM Thomas JA Elements of Information Theory 1991 Wiley-Interscience New York
- [13]Laughlin SBA John C O'Carroll DC de Ruyter van Steveninck RR Baddeley RHR Foldiak R Information Theory and the Brain 2000 Cambridge Univ. Press
- [14]Bryant HL Segundo JP Spike initiation by transmembrane current: a white-noise analysis J. Physiol 1976 260 279 314 978519 PMC1309092
- [15]Mainen ZF Sejnowski TJ Reliability of spike timing in neocortical neurons Science 1995 268 1503 1506 7770778
- [16]Harsch A Robinson HPC Postsynaptic variability of firing in rat cortical neurons: the roles of input synchronization and synaptic NMDA receptor conductance J. Neurosci 2000 20 6181 6192 10934268 PMC6772582
- [17]Schreiber S Fellous J-M Tiesinga P Sejnowski TJ Influence of ionic conductances on spike timing reliability of cortical neurons for suprathreshold rhythmic inputs J. Neurophysiol 2004 91 194 205 14507985 PMC2928819
- [18]de Ruyter van Steveninck RR Lewen GD Strong SP Koberle R Bialek W Reproducibility and variability in neural spike trains Science 1997 275 1805 1808 9065407
- [19]Berry MJ Warland DK Meister M The structure and precision of retinal spike trains Proc. Natl Acad. Sci. USA 1997 94 5411 5416 9144251 PMC24692
- [20]Rieke F Warland D van Steveninck R Bialek W 1997 MIT Press Cambridge, Massachusetts
- [21]Bair W Spike timing in the mammalian visual system Curr. Opin. Neurobiol 1999 9 447 453 10448163
- [22]Keat J Reinagel P Reid RC Meister M Predicting every spike: a model for the responses of visual neurons Neuron 2001 30 803 817 11430813
- [23]Panzeri S Petersen RS Schultz SR Lebedev M Diamond ME The role of spike timing in the coding of stimulus location in rat somatosensory cortex Neuron 2001 29 769 777 11301035
- [24]Kara P Reinagel P Reid RC Low response variability in simultaneously recorded retinal, thalamic and cortical neurons Neuron 2000 27 635 646 11055444
- [25]Billimoria CP DiCaprio RA Birmingham JT Abbott LF Marder E Neuromodulation of spike-timing precision in sensory neurons J. Neurosci 2006 26 5910 5919 16738233 PMC6675233
- [26]Hausser M Spruston N Stuart GJ Diversity and dynamics of dendritic signaling Science 2000 290 739 744 11052929
- [27]Stuart G Hausser M Dendritic coincidence detection of EPSPs and action potentials Nature Neurosci 2001 4 63 71 11135646
- [28]Carr CE Konishi M A circuit for detection of interaural time differences in the brain stem of the barn owl J. Neurosci 1990 10 3227 3246 2213141 PMC6570189
- [29]Fairhall AL Lewen GD Bialek W de Ruyter van Steveninck RR Efficiency and ambiguity in an adaptive neural code Nature 2001 412 787 792 11518957
- [30]Zhurov Y Brezina V Variability of motor neuron spike timing maintains and shapes contractions of the accessory radula closer muscle of aplysia J. Neurosci 2006 26 7056 7070 16807335 PMC6673911
- [31]Bialek W Rieke F de Ruyter van Steveninck RR Warland D Reading a neural code Science 1991 252 1854 1857 2063199
- [32]Softky WR Koch C The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs J. Neurosci 1993 13 334 350 8423479 PMC6576320
- [34]Heggelund P Albus K Response variability and orientation discrimination of single cells in striate cortex of cat Exp. Brain Res 1978 32 197 211 680039
- [35]Shadlen MN Newsome WT The variable discharge of cortical neurons: implications for connectivity, computation, and information coding J. Neurosci 1998 18 3870 3896 9570816 PMC6793166
- [36]Gur M Beylin A Snodderly DM Response variability of neurons in primary visual cortex (V1) of alert monkeys J. Neurosci 1997 17 2914 2920 9092612 PMC6573112
- [37]DeWeese MR Wehr M Zador AM Binary spiking in auditory cortex J. Neurosci 2003 23 7940 7949 12944525 PMC6740590
- [38]Berry MJ Meister M Refractoriness and neural precision J. Neurosci 1998 18 2200 2211 9482804 PMC6792934
- [39]Warzecha A-K Egelhaaf M Variability in spike trains during constant and dynamic stimulation Science 1999 283 1927 1930 10082467
- [40]Azouz R Gray CM Cellular mechanisms contributing to response variability of cortical neurons in vivo J. Neurosci 1999 19 2209 2223 10066274 PMC6782570
- [41]Deweese MR Zador AM Shared and private variability in the auditory cortex J. Neurophysiol 2004 92 1840 1855 15115790
- [42]Calvin WH Stevens CF Synaptic noise and other sources of randomness in motoneuron interspike intervals J. Neurophysiol 1968 31 574 587 5709873
- [44]Schneidman E Freedman B Segev I Ion channel stochasticity may be critical in determining the reliability and precision of spike timing Neural Comput 1998 10 1679 1703 This paper shows that even when large numbers of stochastic ion channels are present in a neuron, fluctuations can become critical near the AP threshold 9744892
- [45]Hodgkin AL Huxley AF Quantitative description of membrane current and its application to conduction and excitation in nerve J. Physiol 1952 117 500 544 12991237 PMC1392413
- [46]Fatt P Katz B Some observations on biological noise Nature 1950 166 597 598 14780165
- [47]Calvin WH Stevens CF Synaptic noise as a source of variability in the interval between action potentials Science 1967 155 842 844 6018196
- [48]Lou X Scheuss V Schneggenburger R Allosteric modulation of the presynaptic Ca2+ sensor for vesicle fusion Nature 2005 435 497 501 15917809
- [49]Derksen HE Verveen AA Fluctuations of resting neural membrane potential Science 1966 151 1388 1389 5909786
- [50]Verveen AA Derksen HE Schick KL Voltage fluctuations of neural membrane Nature 1967 216 588 589 5583506
- [52]Steinmetz PN Manwani A Koch C London M Segev I Subthreshold voltage noise due to channel fluctuations in active neuronal membranes J. Comput. Neurosci 2000 9 133 148 11030518
- [53]White JA Rubinstein JT Kay AR Channel noise in neurons Trends Neurosci 2000 23 131 137 10675918
- [54]van Rossum MC O'Brien BJ Smith RG Effects of noise on the spike timing precision of retinal ganglion cells J. Neurophysiol 2003 89 2406 2419 12740401
- [55]Rubinstein JT Threshold fluctuations in an N sodium channel model of the node of Ranvier Biophys. J 1995 68 779 785 7756544 PMC1281801
- [56]Skaugen E Wallow L Firing behaviour in a stochastic nerve membrane model based upon the Hodgkin-Huxley equations Acta Physiol. Scand 1979 107 343 363 543428
- [57]Chow CC White JA Spontaneous action potentials due to channel fluctuations Biophys. J 1996 71 3012 3021 PMC1233790 8968572
- [58]Diba K Lester HA Koch C Intrinsic noise in cultured hippocampal neurons: experiment and modeling J. Neurosci 2004 24 9723 9733 15509761 PMC6730144
- [59]Jacobson GA Subthreshold voltage noise of rat neocortical pyramidal neurones J. Physiol 2005 564 145 160 15695244 PMC1456039
- [60]Dorval AD Jr White JA Channel noise is essential for perithreshold oscillations in entorhinal stellate neurons J. Neurosci 2005 25 10025 10028 This paper uses an elegant application of the dynamic-clamp technique to show that stochastic effects are required to describe CNS behaviour in vitro 16251451 PMC6725569
- [61]Kole MH Hallermann S Stuart GJ Single Ih channels in pyramidal neuron dendrites: properties, distribution, and impact on action potential output J. Neurosci 2006 26 1677 1687 16467515 PMC6793638
- [62]Schneidman E Freedman B Segev I Ion channel stochasticity may be critical in determining the reliability and precision of spike timing Neural Comput 1998 10 1679 1703 9744892
- [63]Faisal AA White JA Laughlin SB Ion-channel noise places limits on the miniaturization of the brain's wiring Curr. Biol 2005 15 1143 1149 This paper shows that channel noise places a universal lower limit on neuron diameter, matching anatomical data across species 15964281
- [64]Rall W Time constants and electrotonic length of membrane cylinders and neurons Biophys. J 1969 9 1483 1508 5352228 PMC1367649
- [65]Faisal AA Laughlin SB Stochastic simulations on the reliability of action potential propagation in thin axons PLoS Comput. Biol 2007 3 e79 This paper uses stochastic simulations to show that the many thin axons in the CNS are a source of spike-time variability that had been overlooked experimentally. It demonstrated that noise acts in a context-dependent manner and allows for a previously unknown mode of AP conduction 17480115 PMC1864994
- [66]Cecchi GA Noise in neurons is message dependent Proc. Natl Acad. Sci. USA 2000 97 5557 5561 10792057 PMC25867
- [67]Fellous J-M Tiesinga PHE Thomas PJ Sejnowski TJ Discovering spike patterns in neuronal responsess J. Neurosci 2004 24 2989 3001 15044538 PMC2928855
- [68]Horikawa Y Noise effects on spike propagation in the stochastic Hodgkin-Huxley models Biol. Cybern 1991 66 19 25 1768709
- [69]Kandel ER Schwartz JH Jessell TM Principles of Neural Science 2000 McGraw–Hill/Appleton & Lange New York
- [70]Debanne D Information processing in the axon Nature Rev. Neurosci 2004 5 304 316 15034555
- [71]Debanne D Guerineau NC Gahwiler BH Thompson SM Action-potential propagation gated by an axonal IA-like K+ conductance in hippocampus Nature 1997 389 286 289 9305843
- [72]Lecar H Nossal R Theory of threshold fluctuations in nerves. II. Analysis of various sources of membrane noise Biophys. J 1971 11 1068 1084 5167401 PMC1484102
- [73]Manwani A Koch C Detecting and estimating signaals in noisy cable structures. I. Neuronal noise sources Neural Comput 1999 11 1797 1829 10578033
- [74]Jefferys JG Nonsynaptic modulation of neuronal activity in the brain: electric currents and extracellular ions Physiol. Rev 1995 75 689 723 7480159
- [75]Szapiro G Barbour B Multiple climbing fibers signal to molecular layer interneurons exclusively via glutamate spillover Nature Neurosci 2007 10 735 742 17515900
- [76]Katz B Miledi R Membrane noise produced by acetylcholine Nature 1970 226 962 963 5445879
- [77]Kleppe IC Robinson HPC Correlation entropy of synaptic input-output dynamics Phys. Rev. E Stat. Nonlin. Soft Matter Phys 2006 74 041909 17155098
- [78]Faure P Kaplan D Korn H Synaptic efficacy and the transmission of complex firing patterns between neurons J. Neurophysiol 2000 84 3010 3025 11110828
- [79]Steriade M McCormick DA Sejnowski TJ Thalamocortical oscillations in the sleeping and aroused brain Science 1993 262 679 685 8235588
- [80]Cowan RL Wilson CJ Spontaneous firing patterns and axonal projections of single corticostriatal neurons in the rat medial agranular cortex J. Neurophysiol 1994 71 17 32 8158226
- [81]Metherate R Ashe JH Ionic flux contributions to neocortical slow waves and nucleus basalis-mediated activation: whole-cell recordings in vivo J. Neurosci 1993 13 5312 5323 8254377 PMC6576427
- [82]Destexhe A Rudolph M Fellous JM Sejnowski TJ Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons Neuroscience 2001 107 13 24 11744242 PMC3320220
- [84]Stuart GJ Sakmann B Active propagation of somatic action potentials into neocortical pyramidal cell dendrites Nature 1994 367 69 72 8107777
- [85]Wehr M Zador AM Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex Nature 2003 426 442 446 14647382
- [86]Stevens CF Zador AM Input synchrony and the irregular firing of cortical neurons Nature Neurosci 1998 1 210 217 10195145
- [88]Conti R Tan Y Llano I Action potential-evoked and ryanodine-sensitive spontaneous Ca2+ transients at the presynaptic terminal of a developing CNS inhibitory synapse J. Neurosci 2004 24 6946 6957 15295030 PMC6729609
- [89]Wang SQ Song LS Lakatta EG Cheng H Ca2+ signalling between single L-type Ca2+ channels and ryanodine receptors in heart cells Nature 2001 410 592 596 11279498
- [90]Bekkers JM Richerson GB Stevens CF Origin of variability in quantal size in cultured hippocampal neurons and hippocampal slices Proc. Natl Acad. Sci. USA 1990 87 5359 5362 2371276 PMC54323
- [91]Zucker RS Regehr WG Short-term synaptic plasticity Annu. Rev. Physiol 2002 64 355 405 11826273
- [92]Sulzer D Edwards R Vesicles: equal in neurotransmitter concentration but not in volume Neuron 2000 28 5 7 11086975
- [93]Wu X-S The origin of quantal size variation: vesicular glutamate concentration plays a significant role J. Neurosci 2007 27 3046 3056 17360928 PMC6672571
- [95]Nimchinsky EA Yasuda R Oertner TG Svoboda K The number of glutamate receptors opened by synaptic stimulation in single hippocampal spines J. Neurosci 2004 24 2054 2064 14985448 PMC6730404
- [96]Nusser Z Cull-Candy S Farrant M Differences in synaptic GABAA receptor number underlie variation in GABA mini amplitude Neuron 1997 19 697 709 9331359
- [97]Lim R Alvarez FJ Walmsley B Quantal size is correlated with receptor cluster area at glycinergic synapses in the rat brainstem J. Physiol 1999 516 505 512 10087348 PMC2269264
- [98]Paulsson J Summing up the noise in gene networks Nature 2004 427 415 418 14749823
- [99]Abbott LF Regehr WG Synaptic computation Nature 2004 431 796 803 15483601
- [100]Miller P Zhabotinsky AM Lisman JE Wang XJ The stability of a stochastic CaMKII switch: dependence on the number of enzyme molecules and protein turnover PLoS Biol 2005 3 e107 15819604 PMC1069645
- [101]Henneman E Relation between size of neurons and their susceptibility to discharge Science 1957 126 1345 1347 13495469
- [102]Schmidt R Zelaznik H Hawkins B Frank J Quinn J Jr. Motor-output variability: a theory for the accuracy of rapid motor acts Psychol. Rev 1979 47 415 451 504536
- [103]Jones KE Hamilton AF Wolpert DM Sources of signal-dependent noise during isometric force production J. Neurophysiol 2002 88 1533 1544 This paper provides experimental and theoretical evidence that the linear scaling of force variability (signal-dependent noise) is a natural by-product of the organization of motor neurons and muscle fibres (the size principle) 12205173
- [104]Hamilton AF Jones KE Wolpert DM The scaling of motor noise with muscle strength and motor unit number in humans Exp. Brain Res 2004 157 417 430 15014922
- [105]Selen LP Beek PJ van Dieen JH Can co-activation reduce kinematic variability? A simulation study Biol. Cybern 2005 93 373 381 16249892
- [106]Moritz CT Barry BK Pascoe MA Enoka RM Discharge rate variability influences the variation in force fluctuations across the working range of a hand muscle J. Neurophysiol 2005 93 2449 2459 15615827
- [107]Christakos CN Papadimitriou NA Erimaki S Parallel neuronal mechanisms underlying physiological force tremor in steady muscle contractions of humans J. Neurophysiol 2006 95 53 66 16162838
- [108]Moffitt MA McIntyre CC Grill WM Prediction of myelinated nerve fiber stimulation thresholds: limitations of linear models IEEE Trans. Biomed. Eng 2004 51 229 236 14765695
- [109]Mino H Grill WM Jr. Effects of stochastic sodium channels on extracellular excitation of myelinated nerve fibers IEEE Trans. Biomed. Eng 2002 49 527 532 12046697
- [110]Frank TD Friedrich R Beek PJ Stochastic order parameter equation of isometric force production revealed by drift-diffusion estimates Phys. Rev. E Stat. Nonlin. Soft Matter Phys 2006 74 051905 17279937
- [111]Capogrossi MC Stern MD Spurgeon HA Lakatta EG Spontaneous Ca2+ release from the sarcoplasmic reticulum limits Ca2+-dependent twitch potentiation in individual cardiac myocytes. A mechanism for maximum inotropy in the myocardium J. Gen. Physiol 1988 91 133 155 3343586 PMC2216123
- [112]Khatib M Hilaire G Monteau R Excitatory interactions between phrenic motoneurons in the cat Exp. Brain Res 1986 62 273 280 3709712
- [113]Chen H Tourtellotte W Frank E Muscle spindle-derived neurotrophin 3 regulates synaptic connectivity between muscle sensory and motor neurons J. Neurosci 2002 22 3512 3519 11978828 PMC6758400
- [114]Furness J The excitatory input to a single smooth muscle cell Pflugers Arch 1970 314 1 13 4391451
- [115]Morris LG Hooper SL Muscle response to changing neuronal input in the lobster (Panulirus interruptus) stomatogastric system: spike number-versus spike frequency-dependent domains J. Neurosci 1997 17 5956 5971 9221792 PMC6573197
- [116]Lum CS Zhurov Y Cropper EC Weiss KR Brezina V Variability of swallowing performance in intact, freely feeding aplysia J. Neurophysiol 2005 94 2427 2446 15944235 PMC1224712
- [117]Hooper SL Guschlbauer C von Uckermann G Buschges A Natural neural output that produces highly variable locomotory movements J. Neurophysiol 2006 96 2072 2088 16775206
- [118]Ludlow CL Central nervous system control of the laryngeal muscles in humans Respir. Physiol. Neurobiol 2005 147 205 222 15927543 PMC1351146
- [120]Harris CM Wolpert DM The main sequence of saccades optimizes speed-accuracy trade-off Biol. Cybern 2006 95 21 29 16555070 PMC2637438
- [121]van Beers RJ The sources of variability in saccadic eye movements J. Neurosci 2007 27 8757 8 17699658 PMC6672172
- [122]Hamilton AF Wolpert DM Controlling statistics of action: obstacle avoidance J. Neurophysiol 2002 87 2434 2440 11976380
- [123]van Beers RJ Haggard P Wolpert DM The role of execution noise in movement variability J. Neurophysiol 2004 91 1050 1063 14561687
- [124]Kozlov AS Risler T Hudspeth AJ Coherent motion of stereocilia assures the concerted gating of hair-cell transduction channels Nature Neurosci 2007 10 87 92 17173047 PMC2174432
- [126]Glowatzki E Fuchs PA Transmitter release at the hair cell ribbon synapse Nature Neurosci 2002 5 147 154 11802170
- [127]Barlow H Rosenblith WA Sensory Communication 1961 217 234 MIT Press Cambridge, Massachusetts
- [128]Laughlin S A simple coding procedure enhances a neuron's information capacity Z. Naturforsch. [C] 1981 36 910 912 7303823
- [129]Field D Relations between the statistics of natural images and the response properties of cortical cells J. Opt. Soc. Am. A 1987 4 2379 2394 3430225
- [130]Adelman TL Bialek W Olberg RM The information content of receptive fields Neuron 2003 40 823 833 14622585
- [131]Turin G An introduction to matched filters IEEE Trans. Information Theory 1960 6 311 329
- [133]Ernst MO Banks MS Humans integrate visual and haptic information in a statistically optimal fashion Nature 2002 415 429 433 11807554
- [134]Alais D Burr D The ventriloquist effect results from near-optimal bimodal integration Curr. Biol 2004 14 257 262 14761661
- [135]Jacobs RA Optimal integration of texture and motion cues to depth Vision Res 1999 39 3621 3629 This was one of the first studies to demonstrate statistically optimal weighting (weighted averaging) of sensory cues 10746132
- [136]Knill DC Mixture models and the probabilistic structure of depth cues Vision Res 2003 43 831 854 12639607
- [138]Beers RJ Sittig AC Denier JJ How humans combine simultaneous proprioceptive and visual position information Exp. Brain Res 1996 111 253 261 8891655
- [140]Sober S Sabes P Flexible strategies for sensory integration during motor planning Nature Neurosci 2005 8 490 497 15793578 PMC2538489
- [141]Haruno M Wolpert DM Optimal control of redundant muscles in step-tracking wrist movements J. Neurophysiol 2005 94 4244 4255 16079196
- [142]Georgopoulos A Schwartz A Kettner R Neuronal population coding of movement direction Science 1986 233 1416 1419 3749885
- [143]Lee C Rohrer WH Sparks DL Population coding of saccadic eye movements by neurons in the superior colliculus Nature 1988 332 357 360 3352733
- [144]Deneve S Latham P Pouget A Efficient computation and cue integration with noisy population codes Nature Neurosci 2001 4 826 831 11477429
- [145]Zohary E Shadlen MN Newsome WT Correlated neuronal discharge rate and its implications for psychophysical performance 1994 370 140 143 8022482
- [146]Osborne LC Bialek W Lisberger SG Time course of information about motion direction in visual area MT of macaque monkeys J. Neurosci 2004 24 3210 3222 15056700 PMC2553809
- [147]Romo R Hernandez A Zainos A Salinas E Correlated neuronal discharges that increase coding efficiency during perceptual discrimination Neuron 2003 38 649 657 12765615
- [148]Jaynes ET Bretthorst GL Probability Theory: The Logic of Science 2003 Cambridge Univ. Press
- [149]Kording KP Wolpert DM Bayesian integration in sensorimotor learning Nature 2004 427 244 247 This was the first paper to demonstrate that subjects use task statistics (prior knowledge) and knowledge of their sensory variability to produce estimates that are Bayes optimal 14724638
- [150]Stocker AA Simoncelli EP Noise characteristics and prior expectations in human visual speed perception Nature Neurosci 2006 9 578 585 This paper provides a framework for testing how experimental variability can be used to infer prior probabilities as well as the internal noise characteristics directly from experimental data 16547513
- [151]Barlow HB Pattern recognition and the responses of sensory neurons Ann. NY Acad. Sci 1969 156 872 881 5258022
- [152]Barlow H Single units and sensation: a neuron doctrine for perceptual psychology Perception 1972 1 371 394 4377168
- [153]Paradiso M A theory for the use of visual orientation information which exploits the columnar structure of striate cortex Biol. Cybern 1988 58 35 49 3345319
- [155]Hartwell LH Hopfield JJ Leibler S Murray AW From molecular to modular cell biology Nature 1999 402 C47 C42 10591225
- [156]van Hateren JH Laughlin SB Membrane parameters, signal transmission, and the design of a graded potential neuron J. Comp. Physiol. A 1990 166 437 448 2159070
- [157]Niven JE The contribution of Shaker K+ channels to the information capacity of Drosophila photoreceptors Nature 2003 421 630 634 12571596
- [158]Gold JI Shadlen MN Representation of a perceptual decision in developing oculomotor commands Nature 2000 404 390 394 10746726
- [160]Wolpert DM Ghahramani Z Jordan MI An internal model for sensorimotor integration Science 1995 269 1880 1882 7569931
- [161]Kuo A An optimal control model for analyzing human postural balance IEEE Trans. Biomed. Eng 1995 42 87 101 7851935
- [163]Todorov E Optimality principles in sensorimotor control Nature Neurosci. sci 2004 7 907 915 PMC1488877 15332089
- [164]Diedrichsen J Optimal task-dependent changes of bimanual feedback control and adaptation Curr. Biol 2007 17 1675 1679 17900901 PMC2230536
- [165]Liu D Todorov E Evidence for the flexible sensorimotor strategies predicted by optimal feedback control J. Neurosci 2007 27 9354 9368 17728449 PMC6673117
- [166]Gribble PL Mullin LI Cothros N Mattar A Role of cocontraction in arm movement accuracy J. Neurophysiol 2003 89 2396 2405 12611935
- [167]Selen LP Beek PJ van Dieen JH Impedance is modulated to meet accuracy demands during goal-directed arm movements Exp. Brain Res 2006 172 129 138 This paper provided the first experimental evidence that subjects increase joint stiffness when confronted with higher accuracy demands in goal-directed movements. The increase in joint stiffness results in less kinematic variability 16372169
- [168]Selen LP van Dieen JH Beek PJ Impedance modulation and feedback corrections in tracking targets of variable size and frequency J. Neurophysiol 2006 96 2750 2759 16899639
- [169]Lametti DR Houle G Ostry DJ Control of movement variability and the regulation of limb impedance J. Neurophysiol 2007 98 3516 3524 17913978
- [170]Attwell D Laughlin SB An energy budget for signaling in the grey matter of the brain J. Cereb. Blood Flow Metab 2001 21 1133 1145 11598490
- [171]Balasubramanian V Kimber D Berry MJ Metabolically efficient information processing Neural Comput 2001 13 799 815 11255570
- [172]de Polavieja GG Errors drive the evolution of biological signalling to costly codes J. Theor. Biol 2002 214 657 664 11851374
- [176]Longtin A Bulsara A Moss F Time-interval sequences in bistable systems and the noise-induced transmission of information by sensory neurons Phys. Rev. Lett 1991 67 656 659 10044954
- [177]Douglass JK Wilkens L Pantazelou E Moss F Noise enhancement of information transfer in crayfish mechanoreceptors by stochastic resonance 1993 365 337 340 8377824
- [178]Braun HA Wissing H Schafer K Hirsch MC Oscillation and noise determine signal transduction in shark multimodal sensory cells 1994 367 270 273 11407413
- [179]Levin JE Miller JP Broadband neural encoding in the cricket cereal sensory system enhanced by stochastic resonance 1996 380 165 168 8600392
- [180]Cordo P Noise in human muscle spindles 1996 383 769 770 8892999
- [181]Russell DF Wilkens LA Moss F Use of behavioural stochastic resonance by paddle fish for feeding 1999 402 291 294 This paper provided the first demonstration of stochastic resonance effects at the behavioural level 10580499
- [182]Priplata AA Niemi JB Harry JD Lipsitz LA Collins JJ Vibrating insoles and balance control in elderly people Lancet 2003 362 1123 1124 14550702
- [185]Kirkpatrick S Gelatt CD Jr Vecchi MP Optimization by simulated annealing Science 1983 220 671 680 17813860
- [186]Tuckwell HC Rodriguez R Analytical and simulation results for stochastic Fitzhugh-Nagumo neurons and neural networks J. Comput. Neurosci 1998 5 91 113 9540051
- [188]Turrigiano GG Leslie KR Desai NS Rutherford LC Nelson SB Activity-dependent scaling of quantal amplitude in neocortical neurons Nature 1998 391 892 896 9495341
- [189]Prinz A Bucher D Marder E Similar network activity from disparate circuit parameters Nature Neurosci 2004 7 1345 1352 15558066
- [190]Marsalek P Koch C Maunsell J On the relationship between synaptic input and spike output jitter in individual neurons Proc. Natl Acad. Sci. USA 1997 94 735 740 9012854 PMC19583
- [191]Poliakov AV Powers RK Binder MD Functional identification of the input-output transforms of motoneurones in the rat and cat J. Physiol 1997 504 401 424 9365914 PMC1159920
- [192]Sarpeshkar R Analog versus digital: extrapolating from electronics to neurobiology Neural Comp 1998 9 1601 1638 9744889
- [193]Laughlin S van Steveninck RR Anderson J The metabolic cost of neural information Nature Neurosci 1998 1 36 41 10195106
- [194]Laughlin SB Sejnowski TJ Communication in neuronal networks Science 2003 301 1870 1874 14512617 PMC2930149
- [195]Laughlin SB Bock G Good J Complexity in Biological Information Processing 2001 177 187 Wiley Chichester
Cited by
Sorted by citations
Emerging concepts for the dynamical organization of resting-state activity in the brain
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn2961 · Cited by 1,798 · see citing works
Dynamic reconfiguration of human brain networks during learning
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018985108 · Cited by 1,705 · see citing works
Principles of sensorimotor learning
Wolpert, D. M., Diedrichsen, J., & Flanagan, J. R. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn3112 · Cited by 1,391 · see citing works
Self-citationThe cognitive neuroscience of ageing
Grady, C. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3256 · Cited by 1,286 · see citing works
Dynamic models of large-scale brain activity
Breakspear, M. (2017) · Nature Neuroscience · doi:10.1038/nn.4497 · Cited by 1,239 · see citing works
A Survey of Quantization Methods for Efficient Neural Network Inference
Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., & Keutzer, K. (2022) · Low-Power Computer Vision · doi:10.1201/9781003162810-13 · Cited by 1,219 · see citing works
The Temporal Structures and Functional Significance of Scale-free Brain Activity
He, B. J., Zempel, J. M., Snyder, A. Z., & Raichle, M. E. (2010) · Neuron · doi:10.1016/j.neuron.2010.04.020 · Cited by 990 · see citing works
Energy limitation as a selective pressure on the evolution of sensory systems
Niven, J. E., & Laughlin, S. B. (2008) · Journal of Experimental Biology · doi:10.1242/jeb.017574 · Cited by 1,056 · see citing works
Dephasing-assisted transport: quantum networks and biomolecules
Plenio, M. B., & Huelga, S. F. (2008) · New Journal of Physics · doi:10.1088/1367-2630/10/11/113019 · Cited by 1,002 · see citing works
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology
McDonnell, M. D., & Abbott, D. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000348 · Cited by 774 · see citing works
Humans use directed and random exploration to solve the explore–exploit dilemma.
Wilson, R. C., Geana, A., White, J. M., Ludvig, E. A., & Cohen, J. D. (2014) · Journal of Experimental Psychology: General · doi:10.1037/a0038199 · Cited by 580 · see citing works
Nanomesh pressure sensor for monitoring finger manipulation without sensory interference
Lee, S., Franklin, S., Hassani, F. A., Yokota, T., Nayeem, M. O. G., Wang, Y., Leib, R., Cheng, G., Franklin, D. W., & Someya, T. (2020) · Science · doi:10.1126/science.abc9735 · Cited by 640 · see citing works
Voytek, B., & Knight, R. T. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.04.016 · Cited by 556 · see citing works
The mechanisms for pattern completion and pattern separation in the hippocampus
Rolls, E. T. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2013.00074 · Cited by 545 · see citing works
Uncertainty in perception and the Hierarchical Gaussian Filter
Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00825 · Cited by 465 · see citing works
Bayesian just-so stories in psychology and neuroscience.
Bowers, J. S., & Davis, C. J. (2012) · Psychological Bulletin · doi:10.1037/a0026450 · Cited by 436 · see citing works
Bäckman, L., Lindenberger, U., Li, S.-C., & Nyberg, L. (2010) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2009.12.008 · Cited by 414 · see citing works
Blood Oxygen Level-Dependent Signal Variability Is More than Just Noise
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5166-09.2010 · Cited by 396 · see citing works
Exploring the network dynamics underlying brain activity during rest
Cabral, J., Kringelbach, M. L., & Deco, G. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2013.12.005 · Cited by 351 · see citing works
Variance as a signature of neural computations during decision making
Churchland, A. K., Kiani, R., Chaudhuri, R., Wang, X.-J., Pouget, A., & Shadlen, M. N. (2011) · Neuron · doi:10.1016/j.neuron.2010.12.037 · Cited by 350 · see citing works
Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability
Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E., & Pouget, A. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.016 · Cited by 316 · see citing works
Takemura, S.-Y., Xu, C. S., Lu, Z., Rivlin, P. K., Parag, T., Olbris, D. J., Plaza, S., Zhao, T., Katz, W. T., Umayam, L., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509820112 · Cited by 264 · see citing works
Nessler, B., Pfeiffer, M., Buesing, L., & Maass, W. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003037 · Cited by 281 · see citing works
The Modulation of BOLD Variability between Cognitive States Varies by Age and Processing Speed
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs055 · Cited by 276 · see citing works
Neural Coding of Uncertainty and Probability
Ma, W. J., & Jazayeri, M. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-014017 · Cited by 290 · see citing works
Karalunas, S. L., Geurts, H. M., Konrad, K., Bender, S., & Nigg, J. T. (2014) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12217 · Cited by 278 · see citing works
Functional, But Not Anatomical, Separation of “What” and “When” in Prefrontal Cortex
Machens, C. K., Romo, R., & Brody, C. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.3276-09.2010 · Cited by 260 · see citing works
Pattern separation, completion, and categorisation in the hippocampus and neocortex
Rolls, E. T. (2016) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2015.07.008 · Cited by 234 · see citing works
Armbruster, D. J. N., Ueltzhöffer, K., Basten, U., & Fiebach, C. J. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00286 · Cited by 202 · see citing works
Principles of insect path integration
Heinze, S., Narendra, A., & Cheung, A. (2018) · Current Biology · doi:10.1016/j.cub.2018.04.058 · Cited by 195 · see citing works
Brain adaptation and alternative developmental trajectories
Johnson, M. H., Jones, E. J. H., & Gliga, T. (2015) · Development and Psychopathology · doi:10.1017/s0954579415000073 · Cited by 213 · see citing works
Phase-response curves and synchronized neural networks
Smeal, R. M., Ermentrout, G. B., & White, J. A. (2010) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2009.0292 · Cited by 198 · see citing works
From the stochasticity of molecular processes to the variability of synaptic transmission
Ribrault, C., Sekimoto, K., & Triller, A. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn3025 · Cited by 198 · see citing works
Resource-rational decision making
Bhui, R., Lai, L., & Gershman, S. J. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.015 · Cited by 168 · see citing works
Neurocognitive barriers to the embodiment of technology
Makin, T. R., de Vignemont, F., & Faisal, A. A. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-016-0014 · Cited by 165 · see citing works
Self-citationA bioinspired analogous nerve towards artificial intelligence
Liao, X., Song, W., Zhang, X., Yan, C., Li, T., Ren, H., Liu, C., Wang, Y., & Zheng, Y. (2020) · Nature Communications · doi:10.1038/s41467-019-14214-x · Cited by 153 · see citing works
Brain Signal Variability Differentially Affects Cognitive Flexibility and Cognitive Stability
Armbruster-Genç, D. J. N., Ueltzhöffer, K., & Fiebach, C. J. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.2517-14.2016 · Cited by 126 · see citing works
Amphetamine modulates brain signal variability and working memory in younger and older adults
Garrett, D. D., Nagel, I. E., Preuschhof, C., Burzynska, A. Z., Marchner, J., Wiegert, S., Jungehülsing, G. J., Nyberg, L., Villringer, A., Li, S.-C., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1504090112 · Cited by 103 · see citing works
Optimizing walking controllers for uncertain inputs and environments
Wang, J. M., Fleet, D. J., & Hertzmann, A. (2010) · ACM Transactions on Graphics · doi:10.1145/1778765.1778810 · Cited by 113 · see citing works
Interoception as modeling, allostasis as control
Sennesh, E., Theriault, J., Brooks, D., van de Meent, J.-W., Barrett, L. F., & Quigley, K. S. (2022) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108242 · Cited by 107 · see citing works
Hirtzlin, T., Bocquet, M., Penkovsky, B., Klein, J.-O., Nowak, E., Vianello, E., Portal, J.-M., & Querlioz, D. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01383 · Cited by 93 · see citing works
A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia
Anchisi, D., & Zanon, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0117270 · Cited by 84 · see citing works
A virtual rodent predicts the structure of neural activity across behaviours
Aldarondo, D., Merel, J., Marshall, J. D., Hasenclever, L., Klibaite, U., Gellis, A., Tassa, Y., Wayne, G., Botvinick, M., & Ölveczky, B. P. (2024) · Nature · doi:10.1038/s41586-024-07633-4 · Cited by 70 · see citing works
Ferri, F., Costantini, M., Huang, Z., Perrucci, M. G., Ferretti, A., Romani, G. L., & Northoff, G. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.1696-15.2015 · Cited by 65 · see citing works
Studying the neural representations of uncertainty
Walker, E. Y., Pohl, S., Denison, R. N., Barack, D. L., Lee, J., Block, N., Ma, W. J., & Meyniel, F. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01444-y · Cited by 56 · see citing works
Cortical reliability amid noise and chaos
Nolte, M., Reimann, M. W., King, J. G., Markram, H., & Muller, E. B. (2019) · Nature Communications · doi:10.1038/s41467-019-11633-8 · Cited by 56 · see citing works
Discharges in human muscle spindle afferents during a key‐pressing task
Dimitriou, M., & Edin, B. B. (2008) · The Journal of Physiology · doi:10.1113/jphysiol.2008.160036 · Cited by 54 · see citing works
Learning probabilistic neural representations with randomly connected circuits
Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912804117 · Cited by 44 · see citing works
Rolls, E. T. (2012) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2011.06.017 · Cited by 39 · see citing works
Kim, K., Ladenbauer, J., Babo-Rebelo, M., Buot, A., Lehongre, K., Adam, C., Hasboun, D., Lambrecq, V., Navarro, V., Ostojic, S., et al. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2291-18.2019 · Cited by 39 · see citing works
Bayesian inference in ring attractor networks
Kutschireiter, A., Basnak, M. A., Wilson, R. I., & Drugowitsch, J. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2210622120 · Cited by 36 · see citing works
Predicting individual neuron responses with anatomically constrained task optimization
Mano, O., Creamer, M. S., Badwan, B. A., & Clark, D. A. (2021) · Current Biology · doi:10.1016/j.cub.2021.06.090 · Cited by 24 · see citing works
Encoding certainty in bump attractors
Carroll, S., Josić, K., & Kilpatrick, Z. P. (2014) · Journal of Computational Neuroscience · doi:10.1007/s10827-013-0486-0 · Cited by 23 · see citing works
Dreszer, J., Grochowski, M., Lewandowska, M., Nikadon, J., Gorgol, J., Bałaj, B., Finc, K., Duch, W., Kałamała, P., Chuderski, A., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25162 · Cited by 23 · see citing works
State and capacity in neural models of cognition and consciousness
Wang, P., Liu, H., Ilievski, F., Ganushchak, L., Zou, L., Laureys, S., & Paas, F. (2026) · Trends Open · doi:10.1016/j.treopn.2026.04.004 · Cited by 8 · see citing works
Theriault (n.d.) · doi:10.31234/osf.io/pkzr8 · Cited by 51 · see citing works
Kraikivski, P. (2021) · Entropy · doi:10.3390/e23050583 · Cited by 4 · see citing works
Tottori, T., & Kobayashi, T. J. (2025) · Physical Review Research · doi:10.1103/gvl6-cvby · Cited by 6 · see citing works
- Authors
- Faisal, A. A., Selen, L. P. J., & Wolpert, D. M.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Faisal, A. Aldo
- Venue
- Nature Reviews Neuroscience
- Published
- 2008-04
- Volume
- 9
- Issue
- 4
- Pages
- 292-303
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- PMC
- PMC2631351
- OpenAlex
- W2131215403
- Cited by
- 2,883
- References
- 11
- Citations by year
- 2026: 184 · 2025: 228 · 2024: 217 · 2023: 188 · 2022: 195 · 2021: 189 · 2020: 196 · 2019: 178 · 2018: 171 · 2017: 161 · 2016: 149 · 2015: 157 · 2014: 130 · 2013: 141 · 2012: 114
- FWCI
- 4.43
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Funders
- Wellcome Trust (077730)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- neural noise, trial-to-trial variability, behavioral variability, noise sources, neuronal networks, noise countermeasures, information processing, molecular noise
- MeSH terms
- Animals, Humans, Information Theory, Models, Neurological, Nerve Net, Nervous System, Nervous System Physiological Phenomena, Neural Pathways, Noise, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review