Partitioning neuronal variability

The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding

Shadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178 · see citing works

The analysis of visual motion: a comparison of neuronal and psychophysical performance

Britten, K., Shadlen, M., Newsome, W., & Movshon, J. (1992) · The Journal of Neuroscience · doi:10.1523/jneurosci.12-12-04745.1992 · Cited by 1,958 · see citing works

Reliability of spike timing in neocortical neurons

Mainen, Z. F., & Sejnowski, T. J. (1995) · Science · doi:10.1126/science.7770778 · Cited by 1,950 · see citing works

Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity

van Vreeswijk, C., & Sompolinsky, H. (1996) · Science · doi:10.1126/science.274.5293.1724 · Cited by 1,831 · see citing works

The highly irregular firing of cortical cells is inconsistent with temporal integration of random epsps.

Softky, W., & Koch, C. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00334.1993 · Cited by 1,689 · see citing works

Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex

Luck, S. J., Chelazzi, L., Hillyard, S. A., & Desimone, R. (1997) · Journal of Neurophysiology · doi:10.1152/jn.1997.77.1.24 · Cited by 1,615 · see citing works

Spatio-temporal correlations and visual signalling in a complete neuronal population

Pillow, J. W., Shlens, J., Paninski, L., Sher, A., Litke, A. M., Chichilnisky, E. J., & Simoncelli, E. P. (2008) · Nature · doi:10.1038/nature07140 · Cited by 1,315 · see citing works

Correlated neuronal discharge rate and its implications for psychophysical performance

Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994) · Nature · doi:10.1038/370140a0 · Cited by 1,237 · see citing works

Attention improves performance primarily by reducing interneuronal correlations

Cohen, M. R., & Maunsell, J. H. R. (2009) · Nature Neuroscience · doi:10.1038/nn.2439 · Cited by 1,126 · see citing works

Stimulus onset quenches neural variability: A widespread cortical phenomenon

Churchland, M. M., Yu, B. M., Cunningham, J. P., Sugrue, L. P., Cohen, M. R., Corrado, G. S., Newsome, W. T., Clark, A. M., Hosseini, P., Scott, B. B., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2501 · Cited by 1,084 · see citing works

A Point Process Framework for Relating Neural Spiking Activity to Spiking History, Neural Ensemble, and Extrinsic Covariate Effects

Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P., & Brown, E. N. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00697.2004 · Cited by 1,063 · see citing works

Measuring and interpreting neuronal correlations

Cohen, M. R., & Kohn, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2842 · Cited by 1,016 · see citing works

Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables

Polson, N. G., Scott, J. G., & Windle, J. (2013) · Journal of the American Statistical Association · doi:10.1080/01621459.2013.829001 · Cited by 931 · see citing works

An Inquiry into the Nature of Frequency Distributions Representative of Multiple Happenings with Particular Reference to the Occurrence of Multiple Attacks of Disease or of Repeated Accidents

Greenwood, M., & Yule, G. U. (1920) · Journal Of The Royal Statistical Society · doi:10.2307/2341080 · Cited by 847 · see citing works

Nature and Interaction of Signals From the Receptive Field Center and Surround in Macaque V1 Neurons

Cavanaugh, J. R., Bair, W., & Movshon, J. A. (2002) · Journal of Neurophysiology · doi:10.1152/jn.00692.2001 · Cited by 784 · see citing works

Some Statistical Methods Connected with Series of Events

Cox, D. R. (1955) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1955.tb00188.x · Cited by 759 · see citing works

On the actions that one nerve cell can have on another: distinguishing “drivers” from “modulators”

Sherman, S. M., & Guillery, R. W. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.12.7121 · Cited by 746 · see citing works

Correlation between neural spike trains increases with firing rate

de la Rocha, J., Doiron, B., Shea-Brown, E., Josić, K., & Reyes, A. (2007) · Nature · doi:10.1038/nature06028 · Cited by 644 · see citing works

Decorrelated neuronal firing in cortical microcircuits

Ecker, A. S., Berens, P., Keliris, G. A., Bethge, M., Logothetis, N. K., & Tolias, A. S. (2010) · Science · doi:10.1126/science.1179867 · Cited by 640 · see citing works

Spatial and Temporal Scales of Neuronal Correlation in Primary Visual Cortex

Smith, M. A., & Kohn, A. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.2929-08.2008 · Cited by 581 · see citing works

Variability in encoding precision accounts for visual short-term memory limitations

van den Berg, R., Shin, H., Chou, W.-C., George, R. G., & Ma, W. J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117465109 · Cited by 532 · see citing works

Signal Propagation and Logic Gating in Networks of Integrate-and-Fire Neurons

Vogels, T. P., & Abbott, L. F. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.3508-05.2005 · Cited by 466 · see citing works

An evaluation of causes for unreliability of synaptic transmission.

Allen, C., & Stevens, C. F. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.22.10380 · Cited by 406 · see citing works

Low Response Variability in Simultaneously Recorded Retinal, Thalamic, and Cortical Neurons

Kara, P., Reinagel, P., & Reid, R. C. (2000) · Neuron · doi:10.1016/s0896-6273(00)00072-6 · Cited by 404 · 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

Correlations Without Synchrony

Brody, C. D. (1999) · Neural Computation · doi:10.1162/089976699300016133 · Cited by 329 · see citing works

State Dependence of Noise Correlations in Macaque Primary Visual Cortex

Ecker, A. S., Berens, P., Cotton, R. J., Subramaniyan, M., Denfield, G. H., Cadwell, C. R., Smirnakis, S. M., Bethge, M., & Tolias, A. S. (2014) · Neuron · doi:10.1016/j.neuron.2014.02.006 · Cited by 323 · see citing works

MAINTAINED ACTIVITY IN THE CAT'S RETINA IN LIGHT AND DARKNESS

Kuffler, S. W., Fitzhugh, R., & Barlow, H. B. (1957) · The Journal of General Physiology · doi:10.1085/jgp.40.5.683 · Cited by 297 · see citing works

Response variability of neurons in primary visual cortex (V1) of alert monkeys

Gur, M., Beylin, A., & Snodderly, D. M. (1997) · The Journal of Neuroscience · doi:10.1523/jneurosci.17-08-02914.1997 · Cited by 277 · see citing works

The dependence of response amplitude and variance of cat visual cortical neurones on stimulus contrast

Tolhurst, D. J., Movshon, J. A., & Thompson, I. D. (1981) · Experimental Brain Research · doi:10.1007/bf00238900 · Cited by 264 · see citing works

Dynamic Sensory Representations in the Olfactory Bulb: Modulation by Wakefulness and Experience

Kato, H., Chu, M. W., Isaacson, J. S., & Komiyama, T. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.037 · Cited by 259 · see citing works

Stochastic Nature of Precisely Timed Spike Patterns in Visual System Neuronal Responses

Oram, M. W., Wiener, M. C., Lestienne, R., & Richmond, B. J. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.81.6.3021 · Cited by 251 · see citing works

Decoding the activity of neuronal populations in macaque primary visual cortex

Graf, A. B. A., Kohn, A., Jazayeri, M., & Movshon, J. A. (2011) · Nature Neuroscience · doi:10.1038/nn.2733 · Cited by 247 · see citing works

Frequency discrimination in the auditory system: Place or periodicity mechanisms?

Siebert, W. M. (1970) · Proceedings of the IEEE · doi:10.1109/proc.1970.7727 · Cited by 222 · see citing works

Dynamics of motion signaling by neurons in macaque area MT

Smith, M. A., Majaj, N. J., & Movshon, J. A. (2005) · Nature Neuroscience · doi:10.1038/nn1382 · Cited by 200 · see citing works

Construction and analysis of non-Poisson stimulus-response models of neural spiking activity

Barbieri, R., Quirk, M. C., Frank, L. M., Wilson, M. A., & Brown, E. N. (2001) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(00)00344-7 · Cited by 200 · see citing works

Neuronal variability: non-stationary responses to identical visual stimuli

Tomko, G. J., & Crapper, D. R. (1974) · Brain Research · doi:10.1016/0006-8993(74)90438-7 · Cited by 186 · see citing works

Adaptation maintains population homeostasis in primary visual cortex

Benucci, A., Saleem, A. B., & Carandini, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3382 · Cited by 168 · see citing works

Coding Strategies in Monkey V1 and Inferior Temporal Cortices

Gershon, E. D., Wiener, M. C., Latham, P. E., & Richmond, B. J. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.79.3.1135 · Cited by 141 · see citing works

A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings

Pillow, J. W., Shlens, J., Chichilnisky, E. J., & Simoncelli, E. P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062123 · Cited by 133 · see citing works

Modeling the impact of common noise inputs on the network activity of retinal ganglion cells

Vidne, M., Ahmadian, Y., Shlens, J., Pillow, J. W., Kulkarni, J. E., Litke, A. M., Chichilnisky, E. J., Simoncelli, E. P., & Paninski, L. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-011-0376-2 · Cited by 110 · see citing works

Bayesian analysis of identification performance in monkey visual cortex: Nonlinear mechanisms and stimulus certainty

Geisler, W. S., & Albrecht, D. G. (1995) · Vision Research · doi:10.1016/0042-6989(95)00029-y · Cited by 105 · see citing works

Spike Count Reliability and the Poisson Hypothesis

Amarasingham, A., Chen, T., Geman, S., Harrison, M. T., & Sheinberg, D. L. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.2948-05.2006 · Cited by 79 · see citing works

Fully Bayesian inference for neural models with negative-binomial spiking

Scott, J. G., & Pillow, J. W. (2012) · neural information processing systems · Cited by 63 · see citing works

Decoding Spike Trains Instant by Instant Using Order Statistics and the Mixture-of-Poissons Model

Wiener, M. C., & Richmond, B. J. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-06-02394.2003 · Cited by 55 · see citing works

A neural population model for visual pattern detection.

Goris, R. L. T., Putzeys, T., Wagemans, J. P., & Wichmann, F. A. (2013) · Psychological Review · doi:10.1037/a0033136 · Cited by 38 · see citing works

Estimating Membrane Voltage Correlations From Extracellular Spike Trains

Dorn, J. D., & Ringach, D. L. (2003) · Journal of Neurophysiology · doi:10.1152/jn.000889.2002 · Cited by 35 · see citing works

Discharge Patterns of Neurons in Cochlear Nucleus

Grossman, R. G., & Viernstein, L. J. (1961) · Science · doi:10.1126/science.134.3472.99 · Cited by 22 · see citing works

When is an Integrate-and-fire Neuron like a Poisson Neuron?

Stevens, C. F., & Zador, A. M. (1995) · Cited by 17 · see citing works

Spontaneous behaviors drive multidimensional, brainwide activity

Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., & Harris, K. D. (2019) · Science · doi:10.1126/science.aav7893 · Cited by 1,510 · see citing works

A hierarchy of intrinsic timescales across primate cortex

Murray, J. D., Bernacchia, A., Freedman, D. J., Romo, R., Wallis, J. D., Cai, X., Padoa-Schioppa, C., Pasternak, T., Seo, H., Lee, D., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3862 · Cited by 990 · see citing works

Waking state: rapid variations modulate neural and behavioral responses

McGinley, M. J., Vinck, M., Reimer, J., Batista-Brito, R., Zagha, E., Cadwell, C. R., Tolias, A. S., Cardin, J. A., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.012 · Cited by 801 · see citing works

Cortical membrane potential signature of optimal states for sensory signal detection

McGinley, M. J., David, S. V., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.038 · Cited by 728 · see citing works

High-dimensional geometry of population responses in visual cortex

Stringer, C., Pachitariu, M., Steinmetz, N. A., Carandini, M., & Harris, K. D. (2019) · Nature · doi:10.1038/s41586-019-1346-5 · Cited by 591 · see citing works

Information-limiting correlations

Moreno‐Bote, R., Beck, J. M., Kanitscheider, I., Pitkow, X., Latham, P. E., & Pouget, A. (2014) · Nature Neuroscience · doi:10.1038/nn.3807 · Cited by 541 · see citing works

Closed-Loop and Activity-Guided Optogenetic Control

Grosenick, L., Marshel, J. H., & Deisseroth, K. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.034 · Cited by 384 · see citing works

The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep

Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A., & Fiete, I. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0460-x · Cited by 367 · see citing works

Correlations and Neuronal Population Information

Kohn, A., Coen-Cagli, R., Kanitscheider, I., & Pouget, A. (2016) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070815-013851 · Cited by 360 · see citing works

Large-scale neural recordings call for new insights to link brain and behavior

Urai, A. E., Doiron, B., Leifer, A. M., & Churchland, A. K. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00980-9 · Cited by 324 · see citing works

Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis

Williams, A. H., Kim, T. H., Wang, F. L., Vyas, S., Ryu, S. I., Shenoy, K. V., Schnitzer, M. J., Kolda, T. G., & Ganguli, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.015 · Cited by 306 · see citing works

The impact of the human thalamus on brain-wide information processing

Shine, J. M., Lewis, L. D., Garrett, D. D., & Hwang, K. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00701-0 · Cited by 308 · see citing works

Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex

Orbán, G., Berkes, P., Fiser, J., & Lengyel, M. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.038 · Cited by 258 · see citing works

Neural variability: friend or foe?

Dinstein, I., Heeger, D. J., & Behrmann, M. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.04.005 · Cited by 254 · see citing works

The structures and functions of correlations in neural population codes

Panzeri, S., Moroni, M., Safaai, H., & Harvey, C. D. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00606-4 · Cited by 244 · see citing works

The mechanics of state-dependent neural correlations

Doiron, B., Litwin-Kumar, A., Rosenbaum, R., Ocker, G. K., & Josić́, K. (2016) · Nature Neuroscience · doi:10.1038/nn.4242 · Cited by 236 · see citing works

Visual Perceptual Learning and Models

Dosher, B. A., & Lu, Z. (2017) · Annual Review of Vision Science · doi:10.1146/annurev-vision-102016-061249 · Cited by 229 · see citing works

The Nature of Shared Cortical Variability

Lin, I.-C., Okun, M., Carandini, M., & Harris, K. D. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.035 · Cited by 223 · see citing works

Sensory uncertainty decoded from visual cortex predicts behavior

van Bergen, R. S., Ma, W. J., Pratte, M. S., & Jehee, J. F. M. (2015) · Nature Neuroscience · doi:10.1038/nn.4150 · Cited by 217 · see citing works

Selective modulation of cortical state during spatial attention

Engel, T. A., Steinmetz, N. A., Gieselmann, M. A., Thiele, A., Moore, T., & Boahen, K. A. (2016) · Science · doi:10.1126/science.aag1420 · Cited by 207 · see citing works

Locomotion Enhances Neural Encoding of Visual Stimuli in Mouse V1

Dadarlat, M. C., & Stryker, M. P. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.2728-16.2017 · Cited by 205 · see citing works

Effective reinforcement learning following cerebellar damage requires a balance between exploration and motor noise

Therrien, A. S., Wolpert, D. M., & Bastian, A. J. (2015) · Brain · doi:10.1093/brain/awv329 · Cited by 199 · see citing works

Cortical state determines global variability and correlations in visual cortex

Schölvinck, M. L., Saleem, A. B., Benucci, A., Harris, K. D., & Carandini, M. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.4994-13.2015 · Cited by 198 · see citing works

Attention stabilizes the shared gain of V4 populations

Rabinowitz, N. C., Goris, R. L., Cohen, M., & Simoncelli, E. P. (2015) · eLife · doi:10.7554/elife.08998 · Cited by 194 · see citing works

Learning and attention reveal a general relationship between population activity and behavior

Ni, A. M., Ruff, D. A., Alberts, J. J., Symmonds, J., & Cohen, M. R. (2018) · Science · doi:10.1126/science.aao0284 · Cited by 188 · see citing works

The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability

Hennequin, G., Ahmadian, Y., Rubin, D. B., Lengyel, M., & Miller, K. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.04.017 · Cited by 167 · see citing works

Origin of information-limiting noise correlations

Kanitscheider, I., Coen-Cagli, R., & Pouget, A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508738112 · Cited by 160 · see citing works

Synaptic scaling rule preserves excitatory–inhibitory balance and salient neuronal network dynamics

Barral, J., & Reyes, A. D. (2016) · Nature Neuroscience · doi:10.1038/nn.4415 · Cited by 156 · see citing works

Feedback determines the structure of correlated variability in primary visual cortex

Bondy, A., Haefner, R. M., & Cumming, B. G. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0089-1 · Cited by 144 · see citing works

Beyond Trial-Based Paradigms: Continuous Behavior, Ongoing Neural Activity, and Natural Stimuli

Huk, A. C., Bonnen, K., & He, B. J. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1920-17.2018 · Cited by 140 · see citing works

Is neuroimaging measuring information in the brain?

de‐Wit, L., Alexander, D. M., Ekroll, V., & Wagemans, J. P. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-016-1002-0 · Cited by 133 · see citing works

Structures of Neural Correlation and How They Favor Coding

Franke, F., Michele, F., Sevelev, M., Roska, B., Hierlemann, A. R., & da Silveira, R. A. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.037 · Cited by 129 · see citing works

Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex

Cowley, B. R., Snyder, A. C., Acar, K., Williamson, R. C., Yu, B. M., & Smith, M. A. (2020) · Neuron · doi:10.1016/j.neuron.2020.07.021 · Cited by 127 · see citing works

Direction-Selective Circuits Shape Noise to Ensure a Precise Population Code

Zylberberg, J., Cafaro, J., Turner, M. H., Shea‐Brown, E., & Rieke, F. M. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.019 · Cited by 123 · see citing works

Cholinergic shaping of neural correlations

Minces, V. H., Pinto, L., Dan, Y., & Chiba, A. A. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621493114 · Cited by 120 · see citing works

Spontaneous behaviors drive multidimensional, brain-wide activityPreprint

Stringer, C., Pachitariu, M., Steinmetz, N. A., Reddy, C. B., Carandini, M., & Harris, K. D. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/306019 · Cited by 119 · see citing works

Selectivity and tolerance for visual texture in macaque V2

Ziemba, C. M., Freeman, J., Movshon, J. A., & Simoncelli, E. P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510847113 · Cited by 117 · see citing works

Normalization and the Cholinergic Microcircuit: A Unified Basis for Attention

Schmitz, T. W., & Duncan, J. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.02.011 · Cited by 116 · see citing works

Top-down modulation of sensory cortex gates perceptual learning

Caras, M. L., & Sanes, D. H. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1712305114 · Cited by 113 · see citing works

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

Iemi, L., Gwilliams, L., Samaha, J., Auksztulewicz, R., Cycowicz, Y. M., King, J.-R., Nikulin, V. V., Thesen, T., Doyle, W., Devinsky, O., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118746 · Cited by 111 · see citing works

Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains

Zhao, Y., & Park, I. M. (2017) · Neural Computation · doi:10.1162/neco_a_00953 · Cited by 110 · see citing works

Linear dynamical neural population models through nonlinear embeddingsPreprint

Gao, Y., Archer, E., Paninski, L., & Cunningham, J. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08454 · Cited by 109 · see citing works

Multiple timescales of neural dynamics and integration of task-relevant signals across cortex

Spitmaan, M., Seo, H., Lee, D., & Soltani, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005993117 · Cited by 105 · see citing works

Subjective confidence reflects representation of Bayesian probability in cortex

Geurts, L. S., Cooke, J. R. H., van Bergen, R. S., & Jehee, J. F. M. (2022) · Nature Human Behaviour · doi:10.1038/s41562-021-01247-w · Cited by 104 · see citing works

Focality, stochasticity and neuroanatomic propagation in ALS pathogenesis

Ravits, J. (2014) · Experimental Neurology · doi:10.1016/j.expneurol.2014.07.021 · Cited by 104 · see citing works

The effects of delay duration on visual working memory for orientation

Shin, H., Zou, Q., & Ma, W. J. (2017) · Journal of Vision · doi:10.1167/17.14.10 · Cited by 103 · see citing works

Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping

Williams, A. H., Poole, B., Maheswaranathan, N., Dhawale, A. K., Fisher, T., Wilson, C. D., Brann, D. H., Trautmann, E. M., Ryu, S., Shusterman, R., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.10.020 · Cited by 102 · see citing works

Different population dynamics in the supplementary motor area and motor cortex during reaching

Lara, A. H., Cunningham, J. P., & Churchland, M. M. (2018) · Nature Communications · doi:10.1038/s41467-018-05146-z · Cited by 101 · see citing works

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

Williamson, R. C., Cowley, B. R., Litwin-Kumar, A., Doiron, B., Kohn, A., Smith, M. A., & Yu, B. M. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005141 · Cited by 100 · see citing works

Inhibitory control of correlated intrinsic variability in cortical networks

Stringer, C., Pachitariu, M., Steinmetz, N. A., Okun, M., Barthó, P., Harris, K. D., Sahani, M., & Lesica, N. A. (2016) · eLife · doi:10.7554/elife.19695 · Cited by 100 · see citing works

Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models

Weber, A. I., & Pillow, J. W. (2017) · Neural Computation · doi:10.1162/neco_a_01021 · Cited by 97 · see citing works

Dynamics of auditory cortical activity during behavioural engagement and auditory perception

Carcea, I., Insanally, M. N., & Froemke, R. C. (2017) · Nature Communications · doi:10.1038/ncomms14412 · Cited by 96 · see citing works

Asynchronous Rate Chaos in Spiking Neuronal Circuits

Harish, O., & Hansel, D. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004266 · Cited by 96 · see citing works

Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience

Paninski, L., & Cunningham, J. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.04.007 · Cited by 95 · see citing works

Origin and Function of Tuning Diversity in Macaque Visual Cortex

Goris, R. L. T., Simoncelli, E. P., & Movshon, J. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.10.009 · Cited by 95 · see citing works

Neural Variability Quenching Predicts Individual Perceptual Abilities

Arazi, A., Censor, N., & Dinstein, I. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1671-16.2016 · Cited by 92 · see citing works

On the Structure of Neuronal Population Activity under Fluctuations in Attentional State

Ecker, A. S., Denfield, G. H., Bethge, M., & Tolias, A. S. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.2044-15.2016 · Cited by 88 · see citing works

Multiplicative and Additive Modulation of Neuronal Tuning with Population Activity Affects Encoded Information

Arandia-Romero, I., Tanabe, S., Drugowitsch, J., Kohn, A., & Moreno‐Bote, R. (2016) · Neuron · doi:10.1016/j.neuron.2016.01.044 · Cited by 87 · see citing works

Constructing and Forgetting Temporal Context in the Human Cerebral Cortex

Chien, H.-Y. S., & Honey, C. J. (2020) · Neuron · doi:10.1016/j.neuron.2020.02.013 · Cited by 86 · see citing works

Analysis of Neuronal Spike Trains, Deconstructed

Aljadeff, J., Lansdell, B., Fairhall, A. L., & Kleinfeld, D. (2016) · Neuron · doi:10.1016/j.neuron.2016.05.039 · Cited by 83 · see citing works

Feedforward and feedback sources of choice probability in neural population responses

Cumming, B. G., & Nienborg, H. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.01.009 · Cited by 82 · see citing works

Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity

Recanatesi, S., Ocker, G. K., Buice, M. A., & Shea‐Brown, E. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006446 · Cited by 81 · see citing works

Learning and attention increase visual response selectivity through distinct mechanisms

Poort, J., Wilmes, K. A., Blot, A., Chadwick, A., Sahani, M., Clopath, C., Mrsic‐Flogel, T. D., Hofer, S. B., & Khan, A. G. (2021) · Neuron · doi:10.1016/j.neuron.2021.11.016 · Cited by 80 · see citing works

Stimulus complexity shapes response correlations in primary visual cortex

Bányai, M., Lazăr, A., Klein, L., Klon-Lipok, J., Stippinger, M., Singer, W., & Orbán, G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816766116 · Cited by 79 · see citing works

Stochastic sampling provides a unifying account of visual working memory limits

Schneegans, S., Taylor, R. T., & Bays, P. M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2004306117 · Cited by 78 · see citing works

Efficient and adaptive sensory codes

Młynarski, W. F., & Hermundstad, A. M. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00846-0 · Cited by 79 · see citing works

Visual attention mitigates information loss in small- and large-scale neural codes

Sprague, T. C., Saproo, S., & Serences, J. T. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.02.005 · Cited by 79 · see citing works

Fechner’s law in metacognition: A quantitative model of visual working memory confidence.

van den Berg, R., Yoo, A. H., & Ma, W. J. (2017) · Psychological Review · doi:10.1037/rev0000060 · Cited by 79 · see citing works

Neuronal variability reflects probabilistic inference tuned to natural image statistics

Festa, D., Aschner, A., Davila, A., Kohn, A., & Coen-Cagli, R. (2021) · Nature Communications · doi:10.1038/s41467-021-23838-x · Cited by 78 · see citing works

Sensory coding accuracy and perceptual performance are improved during the desynchronized cortical state

Beaman, C. B., Eagleman, S. L., & Dragoi, V. (2017) · Nature Communications · doi:10.1038/s41467-017-01030-4 · Cited by 74 · see citing works

Local temporal variability reflects functional integration in the human brain

Garrett, D. D., Epp, S. M., Perry, A., & Lindenberger, U. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.019 · Cited by 73 · see citing works

Rhythmic Auditory Cortex Activity at Multiple Timescales Shapes Stimulus–Response Gain and Background Firing

Kayser, C., Wilson, C. A., Safaai, H., Sakata, S., & Panzeri, S. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0268-15.2015 · Cited by 70 · see citing works

Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing

Safaai, H., Neves, R. M., Eschenko, O., Logothetis, N. K., & Panzeri, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516539112 · Cited by 68 · see citing works

Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation

Meyer, A. F., Williamson, R. S., Linden, J. F., & Sahani, M. (2017) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00109 · Cited by 68 · see citing works

Neural variability quenching during decision-making: Neural individuality and its prestimulus complexity

Wolff, A., Yao, L., Gómez‐Pilar, J., Shoaran, M., Jiang, N., & Northoff, G. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.070 · Cited by 66 · see citing works

Attentional fluctuations induce shared variability in macaque primary visual cortex

Denfield, G. H., Ecker, A. S., Shinn, T., Bethge, M., & Tolias, A. S. (2018) · Nature Communications · doi:10.1038/s41467-018-05123-6 · Cited by 66 · see citing works

Neural circuit mechanisms for steering control in <i>walking Drosophila</i>

Rayshubskiy, A., Holtz, S. L., Bates, A. S., Vanderbeck, Q. X., Capdevila, L. S., Rockwell, V., & Wilson, R. I. (2020) · doi:10.1101/2020.04.04.024703 · Cited by 64 · see citing works

Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance

Alavash, M., Lim, S.-J., Thiel, C. M., Sehm, B., Deserno, L., & Obleser, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.048 · Cited by 64 · see citing works

Cognition as a Window into Neuronal Population Space

Ruff, D. A., Ni, A. M., & Cohen, M. R. (2018) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-080317-061936 · Cited by 63 · see citing works

Attention-related changes in correlated neuronal activity arise from normalization mechanisms

Verhoef, B.-E., & Maunsell, J. H. R. (2017) · Nature Neuroscience · doi:10.1038/nn.4572 · Cited by 63 · see citing works

Cortical visual prostheses: From microstimulation to functional percept

Najarpour Foroushani, A., Pack, C. C., & Sawan, M. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaa904 · Cited by 62 · see citing works

Stochastic transitions into silence cause noise correlations in cortical circuits

Mochol, G., Hermoso-Mendizabal, A., Sakata, S., Harris, K. D., & de la Rocha, J. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1410509112 · Cited by 62 · see citing works

A muscle-activity-dependent gain between motor cortex and EMG

Naufel, S. N., Glaser, J. I., Kording, K. P., Perreault, E. J., & Miller, L. E. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00329.2018 · Cited by 62 · see citing works

The Magnitude of Trial-By-Trial Neural Variability Is Reproducible over Time and across Tasks in Humans

Arazi, A., Gonen-Yaacovi, G., & Dinstein, I. (2017) · eNeuro · doi:10.1523/eneuro.0292-17.2017 · Cited by 62 · see citing works

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

Hartmann, C., Lazăr, A., Nessler, B., & Triesch, J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004640 · Cited by 60 · see citing works

The Geometry of Information Coding in Correlated Neural Populations

da Silveira, R. A., & Rieke, F. M. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-120320-082744 · Cited by 59 · see citing works

Global network influences on local functional connectivity

Snyder, A. C., Morais, M. J., Willis, C. M., & Smith, M. A. (2015) · Nature Neuroscience · doi:10.1038/nn.3979 · Cited by 59 · see citing works

Multi-Regional Module-Based Signal Transmission in Mouse Visual Cortex

Jia, X., Siegle, J. H., Durand, S., Heller, G. R., Ramirez, T. K., Koch, C., & Olsen, S. R. (2021) · Neuron · doi:10.1016/j.neuron.2022.01.027 · Cited by 57 · see citing works

From the statistics of connectivity to the statistics of spike times in neuronal networks

Ocker, G. K., Hu, Y., Buice, M. A., Doiron, B., Josić́, K., Rosenbaum, R., & Shea‐Brown, E. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.07.011 · Cited by 58 · see citing works

Serotonin Decreases the Gain of Visual Responses in Awake Macaque V1

Seillier, L., Lorenz, C., Kawaguchi, K., Ott, T., Nieder, A., Pourriahi, P., & Nienborg, H. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.1339-17.2017 · Cited by 57 · see citing works

Distinct Waking States for Strong Evoked Responses in Primary Visual Cortex and Optimal Visual Detection Performance

Neske, G. T., Nestvogel, D., Steffan, P. J., & McCormick, D. A. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1226-18.2019 · Cited by 57 · see citing works

Discrete Stepping and Nonlinear Ramping Dynamics Underlie Spiking Responses of LIP Neurons during Decision-Making

Zoltowski, D. M., Latimer, K. W., Yates, J. L., Huk, A. C., & Pillow, J. W. (2019) · Neuron · doi:10.1016/j.neuron.2019.04.031 · Cited by 56 · see citing works

Robust information propagation through noisy neural circuits

Zylberberg, J., Pouget, A., Latham, P. E., & Shea‐Brown, E. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005497 · Cited by 56 · see citing works

Neural Variability Is Quenched by Attention

Arazi, A., Yeshurun, Y., & Dinstein, I. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0355-19.2019 · Cited by 55 · see citing works

The impact of bilateral ongoing activity on evoked responses in mouse cortex

Shimaoka, D., Steinmetz, N. A., Harris, K. D., & Carandini, M. (2019) · eLife · doi:10.7554/elife.43533 · Cited by 54 · see citing works

Representation of visual uncertainty through neural gain variability

Hénaff, O. J., Boundy-Singer, Z. M., Meding, K., Ziemba, C. M., & Goris, R. L. T. (2020) · Nature Communications · doi:10.1038/s41467-020-15533-0 · Cited by 54 · see citing works

Dissociation of Choice Formation and Choice-Correlated Activity in Macaque Visual Cortex

Goris, R. L. T., Ziemba, C. M., Stine, G. M., Simoncelli, E. P., & Movshon, J. A. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3331-16.2017 · Cited by 53 · see citing works

Design strategies for dynamic closed-loop optogenetic neurocontrol in vivo

Bolus, M. F., Willats, A. A., Whitmire, C. J., Rozell, C. J., & Stanley, G. B. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaa506 · Cited by 53 · see citing works

Coordinated neuronal ensembles in primary auditory cortical columns

See, J. Z., Atencio, C. A., Sohal, V. S., & Schreiner, C. E. (2018) · eLife · doi:10.7554/elife.35587 · Cited by 52 · see citing works

Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability

Charles, A. S., Park, M., Weller, J. P., Horwitz, G. D., & Pillow, J. W. (2018) · Neural Computation · doi:10.1162/neco_a_01062 · Cited by 52 · see citing works

Continuous partitioning of neuronal variabilityPreprint

Rupasinghe, A., Charles, A. S., & Pillow, J. W. (2026) · eLife · doi:10.7554/elife.109719.1 · Cited by 1 · see citing works

Is there signal in the noise?

Ecker, A. S., & Tolias, A. S. (2014) · Nature Neuroscience · doi:10.1038/nn.3722 · Cited by 14 · see citing works

Attentional modulation of neuronal variability in circuit models of cortex

Kanashiro, T., Ocker, G. K., Cohen, M. R., & Doiron, B. (2017) · eLife · doi:10.7554/elife.23978 · Cited by 81 · see citing works

Cortical state contributions to neuronal response variability in the early visual cortex: A system identification approach

Sooriyaarachchi, J., Zhan, C. A., & Baker, C. L. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013661

A doubly-stochastic model to analyze neuronal activity in the visual cortex

Anthony, M. J. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/conf.fncom.2012.55.00222