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Stimulus onset quenches neural variability: a widespread cortical phenomenon
Open accessAbstract
Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.
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“Decline of spiking variability at sensory onset is a known universal cortical phenomenon assigned to stabilization of a network state (45).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [45]
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Cites
Shadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178
Self-citationReliability of spike timing in neocortical neurons
Mainen, Z. F., & Sejnowski, T. J. (1995) · Science · doi:10.1126/science.7770778 · Cited by 1,950
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
Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey
Shadlen, M. N., & Newsome, W. T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.4.1916 · Cited by 1,755
Self-citationDynamics of ongoing activity: explanation of the large variability in evoked cortical responses
Arieli, A., Sterkin, A., Grinvald, A., & Aertsen, A. (1996) · Science · doi:10.1126/science.273.5283.1868 · Cited by 1,660
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 1,163
Self-citationNeural Control of Voluntary Movement Initiation
Hanes, D. P., & Schall, J. D. (1996) · Science · doi:10.1126/science.274.5286.427 · Cited by 1,145
Generating Coherent Patterns of Activity from Chaotic Neural Networks
Sussillo, D., & Abbott, L. F. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.018 · Cited by 1,092
A relationship between behavioral choice and the visual responses of neurons in macaque MT
Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S., & Movshon, J. A. (1996) · Visual Neuroscience · doi:10.1017/s095252380000715x · Cited by 1,049
Self-citationMatching Behavior and the Representation of Value in the Parietal Cortex
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004) · Science · doi:10.1126/science.1094765 · Cited by 1,030
Self-citation
Research works printed but not linked: 2
- Abbott, L.F., Rajan, K. & Sompolinsky, H. Interactions between intrinsic and stimulus-dependent activity in recurrent neural networks. in Neuronal Variability and its Functional Significance (eds Ding, M. & Glanzman, D.) (in the press).
- Lee, D. & Seo, H. Neural and behavioral variability related to stochastic choices during a mixed-strategy game. in Neuronal Variability and its Functional Significance (eds Ding, M. & Glanzman, D.) (in the press).
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Cunningham, J. P., & Yu, B. M. (2014) · Nature Neuroscience · doi:10.1038/nn.3776 · Cited by 1,219
Self-citationA 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
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 1,163
Self-citationHarris, K. D., & Thiele, A. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn3084 · Cited by 915
Cortical Control of Arm Movements: A Dynamical Systems Perspective
Shenoy, K. V., Sahani, M., & Churchland, M. M. (2013) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150509 · Cited by 865
Self-citationSpatial attention decorrelates intrinsic activity fluctuations in macaque area V4
Mitchell, J. F., Sundberg, K. A., & Reynolds, J. H. (2009) · Neuron · doi:10.1016/j.neuron.2009.09.013 · Cited by 710
Neural Manifolds for the Control of Movement
Gallego, J. Á., Perich, M. G., Miller, L. E., & Solla, S. A. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.025 · Cited by 676
Shew, W. L., Yang, H., Yu, S. P., Roy, R., & Plenz, D. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.4637-10.2011 · Cited by 599
How advances in neural recording affect data analysis
Stevenson, I. H., & Kording, K. P. (2011) · Nature Neuroscience · doi:10.1038/nn.2731 · Cited by 590
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 565
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
Slow dynamics and high variability in balanced cortical networks with clustered connections
Litwin-Kumar, A., & Doiron, B. (2012) · Nature Neuroscience · doi:10.1038/nn.3220 · Cited by 555
Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine?
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Ryu, S. I., & Shenoy, K. V. (2010) · Neuron · doi:10.1016/j.neuron.2010.09.015 · Cited by 526
Self-citationPartitioning neuronal variability
Goris, R. L. T., Movshon, J. A., & Simoncelli, E. P. (2014) · Nature Neuroscience · doi:10.1038/nn.3711 · Cited by 528
Self-citationCriticality in the brain: A synthesis of neurobiology, models and cognition
Cocchi, L., Gollo, L. L., Zalesky, A., & Breakspear, M. J. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2017.07.002 · Cited by 555
Bottom-Up and Top-Down Attention
Katsuki, F., & Constantinidis, C. (2013) · The Neuroscientist · doi:10.1177/1073858413514136 · Cited by 594
Robust timing and motor patterns by taming chaos in recurrent neural networks
Laje, R., & Buonomano, D. V. (2013) · Nature Neuroscience · doi:10.1038/nn.3405 · Cited by 454
Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks
Mastrogiuseppe, F., & Ostojic, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.07.003 · Cited by 413
Discrete attractor dynamics underlies persistent activity in the frontal cortex
Inagaki, H. K., Fontolan, L., Romani, S., & Svoboda, K. (2019) · Nature · doi:10.1038/s41586-019-0919-7 · Cited by 334
Cortical Areas Interact through a Communication Subspace
Semedo, J. D., Zandvakili, A., Machens, C. K., Yu, B. M., & Kohn, A. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.026 · Cited by 395
Self-citationBuesing, L., Bill, J., Nessler, B., & Maass, W. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002211 · Cited by 384
Recurrent network models of sequence generation and memory
Rajan, K., Harvey, C. D., & Tank, D. W. (2016) · Neuron · doi:10.1016/j.neuron.2016.02.009 · Cited by 351
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
“during Decision Making”
BackgroundMethodResultInfluentialCortical population activity within a preserved neural manifold underlies multiple motor behaviors
Gallego, J. Á., Perich, M. G., Naufel, S. N., Éthier, C., Solla, S. A., & Miller, L. E. (2018) · Nature Communications · doi:10.1038/s41467-018-06560-z · Cited by 355
Neuromodulation of brain state and behavior
McCormick, D. A., Nestvogel, D. B., & He, B. J. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100219-105424 · Cited by 303
Normalization is a general neural mechanism for context-dependent decision making
Louie, K., Khaw, M. W., & Glimcher, P. W. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1217854110 · Cited by 340
Non-invasive transcranial ultrasound stimulation for neuromodulation
Darmani, G., Bergmann, T. O., Pauly, K. B., Caskey, C. F., de Lecea, L., Fomenko, A., Fouragnan, E. F., Legon, W., Murphy, K. R., Nandi, T., et al. (2021) · Clinical Neurophysiology · doi:10.1016/j.clinph.2021.12.010 · Cited by 335
Rubin, D. B., Hooser, S. D. V., & Miller, K. D. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.026 · Cited by 337
Deconstructing multivariate decoding for the study of brain function
Hebart, M. N., & Baker, C. I. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.005 · Cited by 315
Factorial comparison of working memory models.
van den Berg, R., Awh, E., & Ma, W. J. (2014) · Psychological Review · doi:10.1037/a0035234 · Cited by 311
Behavior needs neural variability
Waschke, L., Kloosterman, N. A., Obleser, J., & Garrett, D. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.023 · Cited by 317
Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons
Ostojic, S. (2014) · Nature Neuroscience · doi:10.1038/nn.3658 · Cited by 292
Formation and maintenance of neuronal assemblies through synaptic plasticity
Litwin-Kumar, A., & Doiron, B. (2014) · Nature Communications · doi:10.1038/ncomms6319 · Cited by 311
Stimulus-dependent suppression of chaos in recurrent neural networks
Rajan, K., Abbott, L. F., & Sompolinsky, H. (2010) · Physical Review E · doi:10.1103/physreve.82.011903 · Cited by 278
Learning by neural reassociation
Golub, M. D., Sadtler, P. T., Oby, E. R., Quick, K. M., Ryu, S. I., Tyler‐Kabara, E. C., Batista, A. P., Chase, S. M., & Yu, B. M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0095-3 · Cited by 281
Self-citationSpontaneous and Task-Evoked Brain Activity Negatively Interact
He, B. J. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.2922-12.2013 · Cited by 272
Global waves synchronize the brain’s functional systems with fluctuating arousal
Raut, R. V., Snyder, A. Z., Mitra, A., Yellin, D., Fujii, N., Malach, R., & Raichle, M. E. (2021) · Science Advances · doi:10.1126/sciadv.abf2709 · Cited by 267
Decision making by urgency gating: theory and experimental support
Thura, D., Beauregard-Racine, J., Fradet, C.-W., & Cisek, P. (2012) · Journal of Neurophysiology · doi:10.1152/jn.01071.2011 · Cited by 279
Northoff, G., & Lamme, V. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.07.019 · Cited by 247
Single-Trial Neural Correlates of Arm Movement Preparation
Afshar, A., Santhanam, G., Yu, B. M., Ryu, S. I., Sahani, M., & Shenoy, K. V. (2011) · Neuron · doi:10.1016/j.neuron.2011.05.047 · Cited by 249
Self-citationNeural 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 266
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 275
History-dependent variability in population dynamics during evidence accumulation in cortex
Morcos, A. S., & Harvey, C. D. (2016) · Nature Neuroscience · doi:10.1038/nn.4403 · Cited by 241
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 240
Variability in neural activity and behavior
Renart, A., & Machens, C. K. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.02.013 · Cited by 237
Miura, K., Mainen, Z. F., & Uchida, N. (2012) · Neuron · doi:10.1016/j.neuron.2012.04.021 · Cited by 241
Using Neuronal Populations to Study the Mechanisms Underlying Spatial and Feature Attention
Cohen, M. R., & Maunsell, J. H. R. (2011) · Neuron · doi:10.1016/j.neuron.2011.04.029 · Cited by 223
Self-citationPodvalny, E., Noy, N., Harel, M., Bickel, S., Chechik, G., Schroeder, C. E., Mehta, A. D., Tsodyks, M. V., & Malach, R. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00943.2014 · Cited by 216
Singer, W. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.006 · Cited by 214
Prefrontal Cortex Regulates Sensory Filtering through a Basal Ganglia-to-Thalamus Pathway
Nakajima, M., Schmitt, L. I., & Halassa, M. M. (2019) · Neuron · doi:10.1016/j.neuron.2019.05.026 · Cited by 218
Chen, X., Mu, Y., Hu, Y., Kuan, A. T., Nikitchenko, M., Randlett, O., Chen, A. B., Gavornik, J. P., Sompolinsky, H., Engert, F., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.09.042 · Cited by 199
Two views on the cognitive brain
Barack, D. L., & Krakauer, J. W. (2021) · Nature reviews. Neuroscience · doi:10.1038/s41583-021-00448-6 · Cited by 230
Northoff, G., & Huang, Z. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.07.013 · Cited by 229
Mechanisms of Neuronal Computation in Mammalian Visual Cortex
Priebe, N. J., & Ferster, D. (2012) · Neuron · doi:10.1016/j.neuron.2012.06.011 · Cited by 209
Self-citationSpike Avalanches Exhibit Universal Dynamics across the Sleep-Wake Cycle
Ribeiro, T. L., Copelli, M., Caixeta, F. V., Belchior, H., Chialvo, D. R., Nicolelis, M. A. L., & Ribeiro, S. T. G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014129 · Cited by 200
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
Self-citationZylberberg, J., & Strowbridge, B. W. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070815-014006 · Cited by 202
Packet-based communication in the cortex
Luczak, A., McNaughton, B. L., & Harris, K. D. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn4026 · Cited by 196
Brain mechanisms for perceptual and reward-related decision-making
Deco, G., Rolls, E. T., Albantakis, L., & Romo, R. (2013) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.01.010 · Cited by 196
Computational noise in reward-guided learning drives behavioral variability in volatile environments
Findling, C., Skvortsova, V., Dromnelle, R., Palminteri, S., & Wyart, V. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0518-9 · Cited by 205
The Basal Ganglia Do Not Select Reach Targets but Control the Urgency of Commitment
Thura, D., & Cisek, P. (2017) · Neuron · doi:10.1016/j.neuron.2017.07.039 · Cited by 188
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
Self-citationAlpha/beta power decreases track the fidelity of stimulus-specific information
Griffiths, B., Mayhew, S. D., Mullinger, K. J., Jorge, J., Charest, I., Wimber, M., & Hanslmayr, S. (2019) · eLife · doi:10.7554/elife.49562 · Cited by 184
Wimmer, K., Compte, A., Roxin, A., Peixoto, D., Renart, A., & de la Rocha, J. (2015) · Nature Communications · doi:10.1038/ncomms7177 · Cited by 184
Cortical activity is more stable when sensory stimuli are consciously perceived
Schurger, A., Sarigiannidis, I., Naccache, L., Sitt, J. D., & Dehaene, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418730112 · Cited by 155
Portraits of communication in neuronal networks
Hahn, G., Ponce-Alvarez, A., Deco, G., Aertsen, A., & Kumar, A. (2019) · Nature reviews. Neuroscience · doi:10.1038/s41583-018-0094-0 · Cited by 171
High-precision coding in visual cortex
Stringer, C., Michaelos, M., Tsyboulski, D. A., Lindo, S. E., & Pachitariu, M. (2021) · Cell · doi:10.1016/j.cell.2021.03.042 · Cited by 184
The Rediscovery of Slowness: Exploring the Timing of Cognition
Kringelbach, M. L., McIntosh, A. R., Ritter, P., Jirsa, V., & Deco, G. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.07.011 · Cited by 159
Huang, Z., Zhang, J., Longtin, A., Dumont, G., Duncan, N. W., Pokorny, J., Qin, P., Dai, R., Ferri, F., Weng, X., et al. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv288 · Cited by 170
Neural processing as causal inference
Lochmann, T., & Denève, S. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.05.018 · Cited by 163
Thalamic control of cortical dynamics in a model of flexible motor sequencing
Logiaco, L., Abbott, L. F., & Escola, G. S. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109090 · Cited by 173
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 169
Hennequin, G., Ahmadian, Y., Rubin, D. B., Lengyel, M., & Miller, K. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.04.017 · Cited by 172
Effects of Cue-Triggered Expectation on Cortical Processing of Taste
Samuelsen, C. L., Gardner, M., & Fontanini, A. (2012) · Neuron · doi:10.1016/j.neuron.2012.02.031 · Cited by 156
Posterior Parietal Cortex Guides Visual Decisions in Rats
Licata, A., Kaufman, M. T., Raposo, D. C., Ryan, M., Sheppard, J. P., & Churchland, A. K. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0105-17.2017 · Cited by 152
Herrero, J. L., Gieselmann, M. A., Sanayei, M., & Thiele, A. (2013) · Neuron · doi:10.1016/j.neuron.2013.03.029 · Cited by 154
History-based action selection bias in posterior parietal cortex
Hwang, E. J., Dahlen, J. E., Mukundan, M., & Komiyama, T. (2017) · Nature Communications · doi:10.1038/s41467-017-01356-z · Cited by 158
Why is everyone talking about brain state?
Greene, A. S., Horien, C. L., Barson, D. G., Scheinost, D., & Constable, R. T. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2023.04.001 · Cited by 161
Dynamics of Multistable States during Ongoing and Evoked Cortical Activity
Mazzucato, L., Fontanini, A., & Camera, G. L. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4819-14.2015 · Cited by 156
A Substantial and Unexpected Enhancement of Motion Perception in Autism
Foss‐Feig, J. H., Tadin, D., Schauder, K. B., & Cascio, C. J. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1608-12.2013 · Cited by 152
Bellay, T., Klaus, A., Seshadri, S., & Plenz, D. (2015) · eLife · doi:10.7554/elife.07224 · Cited by 148
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 146
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 149
Northoff, G., & Zilio, F. (2022) · Behavioural Brain Research · doi:10.1016/j.bbr.2022.113788 · Cited by 149
Consciousness is supported by near-critical slow cortical electrodynamics
Toker, D., Pappas, I., Lendner, J. D., Frohlich, J., Mateos, D. M., Muthukumaraswamy, S. D., Carhart-Harris, R. L., Paff, M. R., Vespa, P. M., Monti, M. M., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2024455119 · Cited by 149
The Brain as an Efficient and Robust Adaptive Learner
Denève, S., Alemi, A., & Bourdoukan, R. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.016 · Cited by 143
Stimuli Reduce the Dimensionality of Cortical Activity
Mazzucato, L., Fontanini, A., & Camera, G. L. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00011 · Cited by 134
What is the dynamical regime of cerebral cortex?
Ahmadian, Y., & Miller, K. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.031 · Cited by 146
Serotonin Affects Movement Gain Control in the Spinal Cord
Wei, K., Glaser, J. I., Deng, L., Thompson, C. K., Stevenson, I. H., Wang, Q., Hornby, T. G., Heckman, C. J., & Kording, K. P. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1855-14.2014 · Cited by 135
Ames, K. C., & Churchland, M. M. (2019) · eLife · doi:10.7554/elife.46159 · Cited by 143
Self-citationConditional modeling and the jitter method of spike resampling
Amarasingham, A., Harrison, M. T., Hatsopoulos, N. G., & Geman, S. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00633.2011 · Cited by 134
Spatial working memory alters the efficacy of input to visual cortex
Merrikhi, Y., Clark, K., Albarran, E., Parsa, M., Zirnsak, M., Moore, T., & Noudoost, B. (2017) · Nature Communications · doi:10.1038/ncomms15041 · Cited by 134
Self-citationNeural Network Mechanisms Underlying Stimulus Driven Variability Reduction
Deco, G., & Hugues, E. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002395 · Cited by 132
Wilson–Cowan Equations for Neocortical Dynamics
Cowan, J. D., Neuman, J., & van Drongelen, W. (2016) · The Journal of Mathematical Neuroscience · doi:10.1186/s13408-015-0034-5 · Cited by 135
Canonical computations of cerebral cortex
Miller, K. D. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.01.008 · Cited by 136
Neural reactivations during sleep determine network credit assignment
Gulati, T., Guo, L., Ramanathan, D. S., Bodepudi, A., & Ganguly, K. (2017) · Nature Neuroscience · doi:10.1038/nn.4601 · Cited by 114
Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness
Lo, M.-C., & Widge, A. S. (2017) · International Review of Psychiatry · doi:10.1080/09540261.2017.1282438 · Cited by 125
Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
Echeveste, R., Aitchison, L., Hennequin, G., & Lengyel, M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0671-1 · Cited by 139
Neurocognitive biases and the patterns of spontaneous correlations in the human cortex
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Churchland, M. M., & Shenoy, K. V. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00796-z · Cited by 135
Self-citation
Metadata
- Authors
- 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., Bradley, D. C., Smith, M. A., Kohn, A., Movshon, J. A., Armstrong, K. M., Moore, T., Chang, S. W., Snyder, L. H., Lisberger, S. G., Priebe, N. J., Finn, I. M., Ferster, D., Ryu, S. I., Santhanam, G., Sahani, M., & Shenoy, K. V.
- Affiliations
- Stanford Medicine (US)
- Stanford University (US)
- Howard Hughes Medical Institute (US)
- University of Chicago (US)
- University of Pittsburgh (US)
- Albert Einstein College of Medicine (US)
- New York University (US)
- Washington University in St. Louis (US)
- The University of Texas at Austin (US)
- Northwestern University (US)
- Gatsby Computational Neuroscience Unit (GB)
- Corresponding author
- Churchland, Mark M
- ORCID
- Churchland, Mark M
- Yu, Byron M
- Cunningham, John P
- Sugrue, Leo P
- Cohen, Marlene R
- Corrado, Greg S
- Newsome, William T
- Clark, Andrew M
- Scott, Benjamin B
- Smith, Matthew A
- Kohn, Adam
- Movshon, J Anthony
- Moore, Tirin
- Chang, Steve W
- Snyder, Lawrence H
- Lisberger, Stephen G
- Priebe, Nicholas J
- Ryu, Stephen I
- Sahani, Maneesh
- Shenoy, Krishna V
- Venue
- Nature Neuroscience
- Published
- 2010-02-21
- Volume
- 13
- Issue
- 3
- Pages
- 369-378
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC2828350
- OpenAlex
- W2105380935
- Cited by
- 1,084
- References
- 62
- Citations by year
- 2026: 53 · 2025: 74 · 2024: 74 · 2023: 64 · 2022: 55 · 2021: 86 · 2020: 62 · 2019: 94 · 2018: 54 · 2017: 80 · 2016: 83 · 2015: 69 · 2014: 61 · 2013: 65 · 2012: 52
- FWCI
- 33.17
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (DP1OD006409)
- National Eye Institute (EY 05603, R37EY004440, 1 EY13138-01, R00EY018894, R01EY016774, R01EY003878, EY014924, EY018894, K99EY018894, R01EY002017, EY015958, EY04440, R01EY014924, R01EY013138, R01EY019288, EY-016774, R37EY005603, R01EY004440, F32EY015958, R01EY005603, R56EY014924, EY019288, EY 02017, R01EY004726, R01EY012135)
- National Institute of Neurological Disorders and Stroke (R01NS054283)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- neural response variability, stimulus onset, neuronal firing rate, extracellular recording, intracellular recording, membrane potential, monkeys, cats, anesthetized animals
- MeSH terms
- Action Potentials, Anesthesia, Animals, Cats, Cerebral Cortex, Electrodes, Implanted, Factor Analysis, Statistical, Macaca fascicularis, Macaca mulatta, Macaca nemestrina, Membrane Potentials, Microelectrodes, Motor Activity, Neurons, Neuropsychological Tests, Time Factors, Video Recording, Visual Perception, Wakefulness, Databases, Factual
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