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Variance as a Signature of Neural Computations during Decision Making
Open accessAbstract
Traditionally, insights into neural computation have been furnished by averaged firing rates from many stimulus repetitions or trials. We pursue an analysis of neural response variance to unveil neural computations that cannot be discerned from measures of average firing rate. We analyzed single-neuron recordings from the lateral intraparietal area (LIP), during a perceptual decision-making task. Spike count variance was divided into two components using the law of total variance for doubly stochastic processes: (1) variance of counts that would be produced by a stochastic point process with a given rate, and loosely (2) the variance of the rates that would produce those counts (i.e., "conditional expectation"). The variance and correlation of the conditional expectation exposed several neural mechanisms: mixtures of firing rate states preceding the decision, accumulation of stochastic "evidence" during decision formation, and a stereotyped response at decision end. These analyses help to differentiate among several alternative decision-making models.
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“When VarCE is calculated from residuals (5, 51) (Materials and Methods), it is similar (Fig. 4G, black) showing linear increase between decision onset (Fig. 4G, up arrow) and decision termination (Fig. 4G, down arrow) times.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Analysis of spike count noise during this period showed characteristic linear increase in internally driven spike rate variability (VarCE) and decrease of its correlations (CorCE) consistent with the gradual accumulation models of decision-making (51).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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“indicating accumulation of stochastic trial-by-trial diffusion noise (5, 51).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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“An important diagnostic signature of perceptual decision-making is the gradual accumulation of noise (5, 51) expected for most accumulation-to-bound decision models.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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“Total variance of this doubly stochastics process becomes a sum of two separate components (51) (Materials and Methods): PPV, proportional to mean spike rates, and variance of conditional expectation (VarCE), reflecting variance of rates.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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“It has been argued (51) that if the variance of spike rates during evidence accumulation is defined by a diffusion process, then expectation of spike counts in consecutive time bins (CorCE) should covary.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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“The calculated (Materials and Methods) CorCE matrix (Fig. 4J) indeed exhibits characteristic increase of correlation as a function of time and, for given time (e.g., along the top row), shows sequential decay as often observed (5, 51) during decision-making.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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“Overall, VarCE exhibits a linear increase during epoch 4, a characteristic signature of diffusion process consistent with an accumulation-to-bound model (5, 51).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [51]
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Citations
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“In summary, the dip in VarCE prior to decision onset is probably not a correlate of the resetting of the integrator.”
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“For instance, it is broadly observed that as neural activity increases, the signal-to-noise ratio of decoded neural quantities also increases (e.g., McAdams andMaunsell 1999, Churchland et al. 2011).”
A neural ensemble correlation code for sound category identification
Najafabadi, M. S., Zhai, X., Stevenson, I. H., & Escabı́, M. A. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000449 · Cited by 30
Cortical response states for enhanced sensory discrimination
Gutnisky, D. A., Beaman, C. B., Lew, S. E., & Dragoi, V. (2017) · eLife · doi:10.7554/elife.29226 · Cited by 21
“Additionally, we examined the relationship between the response magnitude and variability of single neurons by calculating the neurons’ Fano factor (Churchland et al., 2010; Goris et al., 2014; Churchland et al., 2011) in the low and high pre-stimulus trials.”
MethodWijdenes, L. O., Ivry, R. B., & Bays, P. M. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00113.2016 · Cited by 24
“Mirroring the findings on firing rates for items in working memory, nonhuman primate electrophysiology studies have shown that average firing rates decrease and firing rate variability increases when the number of response alternatives increases (Churchland and Ditterich 2012; Churchland et al. 2011).”
BackgroundFalkner, A. L., Goldberg, M. E., & Krishna, B. S. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.5269-12.2013 · Cited by 23
“…approach based on trial-by-trial variations in firing rate has been used before to characterize Fano factors 1 in hippocampal neurons (Olypher et al., 2002) and recently in LIP itself to differentiate between different models of decision making (Shadlen and Newsome, 1998; Churchland et al., 2011).”
BackgroundMethodInfluentialA unified framework for perceived magnitude and discriminability of sensory stimuli
Zhou, J., Duong, L., & Simoncelli, E. P. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2312293121 · Cited by 22
“…model has variance with both linear and quadratic terms, and can capture the relationship of spike count variance to mean response over the extended range [51, 52]: where the constant g governs the transition from the Poisson range (smaller µ ( s )) to the super-Poisson range (larger µ ( s )) (Fig.”
BackgroundGain Control in the Auditory Cortex Evoked by Changing Temporal Correlation of Sounds
Natan, R. G., Carruthers, I. M., Mwilambwe-Tshilobo, L., & Geffen, M. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw083 · Cited by 21
“The Fano factor provides a quantification of the variability in neuronal responses to the stimulus (Churchland et al. 2011).”
BackgroundSeparating Spike Count Correlation from Firing Rate Correlation
Vinci, G., Ventura, V., Smith, M. A., & Kass, R. E. (2016) · Neural Computation · doi:10.1162/neco_a_00831 · Cited by 23
“…of intermediate quantities are unbiased; we also derive a nonparametric estimator of φi from spike trains, in contrast to Goris et al. (2014), who assume φi = 1, and Churchland et al. (2011), who use φi like a tuning parameter and choose it such that variances and correlations are well defined.”
BackgroundInfluentialThe Jacobi diffusion process as a neuronal model.
D’Onofrio, G., Tamborrino, M., & Lánský, P. (2018) · Chaos · doi:10.1063/1.5051494 · Cited by 19
Dynamic and stochastic models of neuroimaging data: A comment on Lohmann et al.
Breakspear, M. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.047 · Cited by 14
“Furthermore, it has been suggested that neuronal variability may itself play a crucial cognitive role during decision-making (Churchland et al., 2011).”
BackgroundTargeted V1 comodulation supports task-adaptive sensory decisions
Haimerl, C., Ruff, D., Cohen, M. R., Savin, C., & Simoncelli, E. P. (2021) · bioRxiv · doi:10.1101/2021.02.23.432351 · Cited by 18
“Overall modulation strength in the population is determined by the modulator variance (var(mt) = σ(2) m - see also [8]).”
BackgroundJoshua, M., & Lisberger, S. G. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00510.2013 · Cited by 19
“The velocity-to-position neural integrator lies between FTNs and abducens neurons, and integration could accumulate variation as a function of time (Churchland et al. 2011).”
BackgroundThe Timing of Vision – How Neural Processing Links to Different Temporal Dynamics
Masquelier, T., Albantakis, L., & Deco, G. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00151 · Cited by 21
“Several recent studies have acknowledged this objective with a particular emphasis on multiple alternatives (Ditterich, 2010; Leite and Ratcliff, 2010; Purcell et al., 2010; Churchland et al., 2011).”
BackgroundA mathematical theory of relational generalization in transitive inferencePreprint
Lippl, S., Kay, K., Jensen, G., Ferrera, V., & Abbott, L. (2023) · bioRxiv · doi:10.1101/2023.08.22.554287 · Cited by 17
Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making
Barranca, V. J., Bhuiyan, A., Sundgren, M., & Xing, F. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.801847 · Cited by 17
“Large spike count variability across controlled trials is commonly observed in cortical neurons in evoked conditions (Shadlen and Newsome, 1994; Churchland et al., 2011), and such stochasticity in spike emission is seen in balanced networks that obey Dale’s law (Tsodyks et al.”
BackgroundZhang, Z., Cheng, H., & Yang, T. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008342 · Cited by 23
“Analogous to the variance of conditional expectation (VarCE) that Churchland and colleagues studied previously in the LIP neurons [21], we calculate the response variability of the units in the hidden layer (Fig 5D).”
BackgroundResultInfluentialUrgency, Leakage, and the Relative Nature of Information Processing in Decision-making
Trueblood, J., Heathcote, A., Evans, N., & Holmes, W. (2019) · bioRxiv · doi:10.1101/706291 · Cited by 17
“A more common assumption, made to our knowledge in all earlier work on decisions on the direction of motion (Ratcliff & McKoon, 2008; Roitman & Shadlen, 2002), is that evidence sampled at a given time is associated with object motion (e.g., that a dot moves to the left or to the right, see Figure 12d for the evidence…”
BackgroundInfluentialDiscrete attractor dynamics underlying selective persistent activity in frontal cortexPreprint
Inagaki, H. K., Fontolan, L., Romani, S., & Svoboda, K. (2017) · bioRxiv · doi:10.1101/203448 · Cited by 17
Cause for pause before leaping to conclusions about steppingPreprint
Zylberberg, A., & Shadlen, M. N. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/085886 · Cited by 17
“, a linear increase as a function of time) across trials and the autocorrelation of x(t) within trials (a function of both the lag between samples and the time of the first sample) [8, 9].”
BackgroundSelf-citationNeurocognitive Modeling of Perceptual Decision Making
Palmeri, T. J., Schall, J. D., & Logan, G. D. (2015) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199957996.013.15 · Cited by 17
Learning probability distributions of sensory inputs with Monte Carlo predictive coding
Oliviers, G., Bogacz, R., & Meulemans, A. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012532 · Cited by 10
Neural population dynamics underlying evidence accumulation in multiple rat brain regions
DePasquale, B. D., Brody, C., & Pillow, J. (2021) · bioRxiv · doi:10.1101/2021.10.28.465122 · Cited by 16
“The logic underlying this work (e.g., Churchland et al., 2011; Gold and Shadlen, 2007; Hanks et al., 2015; Mante et al., 2013; Ratcliff et al., 2003; Yartsev et al., 2018) is that behavior is well approximated by gradual evidence accumulation (Ratcliff and McKoon, 2008).”
InfluentialLower neuronal variability in the monkey dorsolateral prefrontal than posterior parietal cortex
Qi, X.-L., & Constantinidis, C. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00454.2015 · Cited by 14
Saccadic brightness decisions do not use a difference model
Liston, D. B., & Stone, L. S. (2013) · Journal of Vision · doi:10.1167/13.8.1 · Cited by 17
Adjusted regularization in latent graphical models: Application to multiple-neuron spike count data
Vinci, G., Ventura, V., Smith, M. A., & Kass, R. E. (2018) · The Annals of Applied Statistics · doi:10.1214/18-aoas1190 · Cited by 9
Benyamini, M., & Zacksenhouse, M. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00071 · Cited by 20
Abstract deliberation by visuomotor neurons in prefrontal cortex
Charlton, J. A., & Goris, R. L. T. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01635-1 · Cited by 28
“A neural signature of this effect can sometimes be seen in the timecourse of residual response variance (that is, response variance that is not due to choice outcome or stimulus strength) 26 .”
BackgroundMarkicevic, M., Sturman, O., Bohacek, J., Rudin, M., Zerbi, V., Fulcher, B., & Wenderoth, N. (2023) · eLife · doi:10.7554/elife.78620 · Cited by 14
“…has shown that different regions exhibit varied timescales of fluctuations in neuronal population activity, that is, magnitude of autocorrelation ( Churchland et al., 2011 ; Murray et al., 2014 ), which varied in the presence of a stimulus or a task ( Nougaret et al., 2021 ; Runyan et al., 2017 ;…”
BackgroundZhao, Y., Ma, T., Ran, X., Zhang, L., Zhang, R., & Ku, Y. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009544 · Cited by 17
Task-specific modulation of PFC activity for matching-rule governed decision-making
Parto-Dezfouli, M., Zarei, M. M., Constantinidis, C., & Daliri, M. R. (2021) · Brain Structure and Function · doi:10.1007/s00429-020-02191-7 · Cited by 16
Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions
Balaguer‐Ballester, E., Nogueira, R., Abofalia, J. M., Moreno‐Bote, R., & Sanchez-Vives, M. V. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007862 · Cited by 14
Learning nonnative speech sounds changes local encoding in the adult human cortex
Yi, H., Chandrasekaran, B., Nourski, K. V., Rhone, A. E., Schuerman, W. L., Howard, M. A., Chang, E. F., & Leonard, M. K. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2101777118 · Cited by 13
“In the present study, a higher mean-variance relationship in learning populations may indicate that acoustic encoding is more flexible than in populations that do not track trial-by-trial behavior (75, 77, 78).”
BackgroundDiscerning Mouse Trajectory Features With the Drift Diffusion Model
Leontyev, A., & Yamauchi, T. (2020) · Cognitive Sciences · doi:10.31234/osf.io/cn8q7 · Cited by 13
Fractal spike dynamics and neuronal coupling in the primate visual system
Munn, B., Zeater, N., Pietersen, A. N. J., Solomon, S. G., Cheong, S. K., Martin, P. R., & Gong, P. (2020) · The Journal of Physiology · doi:10.1113/jp278935 · Cited by 13
“Neuronal spike patterns are highly variable, and display substantial diversity across neurons, during both maintained activity and evoked responses (Softky & Koch, 1993; Churchland et al. 2011).”
BackgroundMethodSpatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex
Logiaco, L., Quilodran, R., Procyk, E., & Arleo, A. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002222 · Cited by 17
The Influence of Spatiotemporal Structure of Noisy Stimuli in Decision Making
Insabato, A., Dempere‐Marco, L., Pannunzi, M., Deco, G., & Romo, R. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003492 · Cited by 15
“An excellent example of this joined effort is illustrated by Churchland et al. [103].”
MethodNeural basis of concurrent deliberation toward a choice and confidence judgment
Vivar-Lazo, M., & Fetsch, C. R. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02116-9 · Cited by 22
“To dig deeper into the nature of the observed ramping signals, we tested for statistical signatures of a bounded accumulation process 48,49 —(1) increasing variance of the underlying rate (variance of the conditional expectation, VarCE) followed by a collapse near decision termination, and (2) a…”
Li, Z.-L., Andreev, A., Hramov, A., Blyuss, O., & Zaikin, A. (2024) · Nonlinear dynamics · doi:10.1007/s11071-024-10244-3 · Cited by 12
Sound improves neuronal encoding of visual stimuli in mouse primary visual cortexPreprint
Williams, A. M., Angeloni, C., & Geffen, M. (2021) · bioRxiv · doi:10.1101/2021.08.03.454738 · Cited by 12
“It is therefore important to consider response variability in awake brains in addition to magnitude-based metrics when quantifying tuning and discriminability in neurons (Churchland et al., 2011; Mazurek et al., 2014).”
BackgroundModeling stimulus-dependent variability improves decoding of population neural responses
Ghanbari, A., Lee, C. M., Read, H. L., & Stevenson, I. H. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab3a68 · Cited by 12
Value-based decisions involve sequential sampling from memoryPreprint
Bakkour, A., Zylberberg, A., Shadlen, M. N., & Shohamy, D. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/269290 · Cited by 16
Self-citation
Metadata
- Authors
- Churchland, A. K., Kiani, R., Chaudhuri, R., Wang, X.-J., Pouget, A., & Shadlen, M. N.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Yale University (US)
- University of Rochester (US)
- Corresponding author
- Churchland, Anne. K.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 69
- Issue
- 4
- Pages
- 818-831
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC3066020
- OpenAlex
- W2105007656
- Cited by
- 350
- References
- 56
- Citations by year
- 2026: 7 · 2025: 19 · 2024: 27 · 2023: 15 · 2022: 20 · 2021: 25 · 2020: 21 · 2019: 20 · 2018: 20 · 2017: 22 · 2016: 30 · 2015: 35 · 2014: 28 · 2013: 20 · 2012: 33
- FWCI
- 14.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- James S. McDonnell Foundation (P30 EY001319)
- National Institutes of Health (RR001 66, EY001319)
- U.S. Public Health Service (BCS0346785)
- Multidisciplinary University Research Initiative (N00014-07-1-0937)
- National Institute of Mental Health (MH 062349, R01MH062349)
- National Institute on Drug Abuse (DA022780, R01DA022780)
- National Eye Institute (EY011378, EY019072, R00EY019072, K99EY019072, R01EY011378, P30EY001319)
- National Center for Research Resources (RR00166, P51RR000166)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- perceptual decision making, lateral intraparietal area, law of total variance, doubly stochastic processes, conditional expectation, single-unit recording, decision models
- MeSH terms
- Action Potentials, Animals, Haplorhini, Computer Simulation, Decision Making, Models, Neurological, Motion Perception, Neurons, Numerical Analysis, Computer-Assisted, Photic Stimulation, Reaction Time, Statistics as Topic, Stochastic Processes, Time Factors
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