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Spontaneous and driven cortical activity: implications for computation.
Open accessAbstract
The traditional view of spontaneous neural activity as 'noise' has been challenged by recent findings suggesting that: (a) spontaneous activity in cortical populations is highly structured in both space and time, (b) the spatio-temporal structure of spontaneous activity is linked to the underlying connectivity of the cortical network, (c) spontaneous cortical activity interacts with external stimulation to generate responses to the individual presentations of a stimulus, (d) network connectivity is shaped in part by the statistics of natural signals and (e) ongoing cortical activity represents a continuous top-down prediction/expectation signal that interacts with incoming input to generate an updated representation of the world. These results can be integrated to provide a new framework for the study of cortical computation.
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“several experimental studies arrive at the conclusion that external stimuli only modulate the highly stochastic spontaneous firing activity of cortical networks of neurons [7], [8].”
From Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons (2011) · cites this paper as [8]
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Cites
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483
Intrinsic functional architecture in the anaesthetized monkey brain
Vincent, J. L., Patel, G. H., Fox, M. D., Snyder, A. Z., Baker, J. T., Van Essen, D. C., Zempel, J. M., Snyder, L. H., Corbetta, M., & Raichle, M. E. (2007) · Nature · doi:10.1038/nature05758 · Cited by 1,786
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
Dynamics 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
Hierarchical Bayesian inference in the visual cortex
Lee, T. S., & Mumford, D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001434 · Cited by 1,473 · cited 1×
“One way of thinking about the integration of information is via Bayesian inference, where the V1 circuitry works to find the state within the manifold that maximizes the conditional probability, p(XjY, Z), under some assumptions [46], p(XjY, Z) p(XjY)p(XjZ).”
Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex
Ohki, K., Chung, S., Ch'ng, Y. H., Kara, P., & Reid, R. C. (2005) · Nature · doi:10.1038/nature03274 · Cited by 1,191
Orientation Selectivity and the Arrangement of Horizontal Connections in Tree Shrew Striate Cortex
Bosking, W. H., Zhang, Y., Schofield, B. R., & Fitzpatrick, D. (1997) · Journal of Neuroscience · doi:10.1523/jneurosci.17-06-02112.1997 · Cited by 1,115
Theory of orientation tuning in visual cortex.
Ben-Yishai, R., Bar‐Or, R. L., & Sompolinsky, H. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.9.3844 · Cited by 1,091
Brain states: top-down influences in sensory processing.
Gilbert, C., & Sigman, M. (2007) · Neuron · doi:10.1016/j.neuron.2007.05.019 · Cited by 1,008
The statistical reliability of signals in single neurons in cat and monkey visual cortex
Tolhurst, D. J., Movshon, J. A., & Dean, A. F. (1983) · Vision Research · doi:10.1016/0042-6989(83)90200-6 · Cited by 1,002
Cited by
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Dynamic functional connectivity: Promise, issues, and interpretations
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962
Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906
“The membrane potential of neurons is not only dependent on the afferent signals that are meant to drive the neuron but also on other signals originating from the rest of the brain in the form of ongoing activity”
BackgroundSpontaneous 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
Harris, K. D., & Thiele, A. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn3084 · Cited by 915
Statistically optimal perception and learning: from behavior to neural representations
Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672
Cellular mechanisms of brain state-dependent gain modulation in visual cortex
Polack, P.-O., Friedman, J., & Golshani, P. (2013) · Nature Neuroscience · doi:10.1038/nn.3464 · Cited by 628
Hutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S., & Menon, R. S. (2012) · Human Brain Mapping · doi:10.1002/hbm.22058 · Cited by 732
Generation of a synthetic memory trace
Garner, A. R., Rowland, D. C., Hwang, S. Y., Baumgaertel, K., Roth, B. L., Kentros, C., & Mayford, M. (2012) · Science · doi:10.1126/science.1214985 · Cited by 387
Buesing, L., Bill, J., Nessler, B., & Maass, W. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002211 · Cited by 384
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
Traveling Waves in Visual Cortex
Sato, T. K., Nauhaus, I., & Carandini, M. (2012) · Neuron · doi:10.1016/j.neuron.2012.06.029 · Cited by 288
Dynamic BOLD functional connectivity in humans and its electrophysiological correlates
Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., & Laufs, H. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00339 · Cited by 300
Advances in fMRI Real-Time Neurofeedback
Watanabe, T., Sasaki, Y., Shibata, K., & Kawato, M. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.09.010 · Cited by 242
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
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 232
NMDA Receptor Regulation Prevents Regression of Visual Cortical Function in the Absence of Mecp2
Durand, S., Patrizi, A., Quast, K. B., Hachigian, L., Pavlyuk, R., Saxena, A., Carninci, P., Hensch, T. K., & Fagiolini, M. (2012) · Neuron · doi:10.1016/j.neuron.2012.12.004 · Cited by 172
Lag threads organize the brain’s intrinsic activity
Mitra, A., Snyder, A. Z., Blazey, T., & Raichle, M. E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1503960112 · Cited by 191
Can Innate, Modular “Foundations” Explain Morality? Challenges for Haidt's Moral Foundations Theory
Suhler, C. L., & Churchland, P. S. (2011) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2011.21637 · Cited by 205
Miskovic, V., Ma, X., Chou, C.-A., Fan, M., Owens, M., Sayama, H., & Gibb, B. E. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.013 · Cited by 175
Bring the Noise: Reconceptualizing Spontaneous Neural Activity
Uddin, L. Q. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.06.003 · Cited by 179
The secret life of predictive brains: what’s spontaneous activity for?
Pezzulo, G., Zorzi, M., & Corbetta, M. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.05.007 · Cited by 173
Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance
Cortese, A., Amano, K., Koizumi, A., Kawato, M., & Lau, H. (2016) · Nature Communications · doi:10.1038/ncomms13669 · Cited by 157
“However, we cannot rule out the possibility that DecNef-induced neural patterns might have carried over to the post-DecNef psychophysical test period as a form of spontaneous brain activity 57,58 , without a single, particular association with dot motion.”
Brain activity mapping at multiple scales with silicon microprobes containing 1,024 electrodes
Shobe, J. L., Claar, L. D., Parhami, S., Bakhurin, K. I., & Masmanidis, S. C. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00464.2015 · Cited by 152
Spontaneous Neuronal Network Dynamics Reveal Circuit’s Functional Adaptations for Behavior
Romano, S. A., Pietri, T., Pérez-Schuster, V., Jouary, A., Haudrechy, M., & Sumbre, G. (2015) · Neuron · doi:10.1016/j.neuron.2015.01.027 · Cited by 138
Megumi, F., Yamashita, A., Kawato, M., & Imamizu, H. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00160 · Cited by 143
State-Dependent Population Coding in Primary Auditory Cortex
Pachitariu, M., Lyamzin, D. R., Sahani, M., & Lesica, N. A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3318-14.2015 · Cited by 144
Lim, D. H., Mohajerani, M. H., LeDue, J. M., Boyd, J. D., Chen, S., & Murphy, T. H. (2012) · Frontiers in Neural Circuits · doi:10.3389/fncir.2012.00011 · Cited by 142
Facemap: a framework for modeling neural activity based on orofacial tracking
Syeda, A., Zhong, L., Tung, R., Long, W., Pachitariu, M., & Stringer, C. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01490-6 · Cited by 157
Nagasawa, T., Juhász, C., Rothermel, R., Hoechstetter, K., Sood, S., & Asano, E. (2011) · Human Brain Mapping · doi:10.1002/hbm.21233 · Cited by 129
Attractors and noise: Twin drivers of decisions and multistability
Braun, J., & Mattia, M. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.126 · Cited by 124
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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 123
Avitan, L., Pujic, Z., Mölter, J., Poll, M. N. V. D., Sun, B., Teng, H., Amor, R., Scott, E. K., & Goodhill, G. J. (2017) · Current Biology · doi:10.1016/j.cub.2017.06.056 · Cited by 107
Role of mutual inhibition in binocular rivalry
Seely, J., & Chow, C. C. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00228.2011 · Cited by 100
Nienborg, H., & Cumming, B. G. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.05.002 · Cited by 104
Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback
Shibata, K., Lisi, G., Cortese, A., Watanabe, T., Sasaki, Y., & Kawato, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.022 · Cited by 107
Natural grouping of neural responses reveals spatially segregated clusters in prearcuate cortex
Kiani, R., Cueva, C. J., Reppas, J., Peixoto, D., Ryu, S., & Newsome, W. (2015) · Neuron · doi:10.1016/j.neuron.2015.02.014 · Cited by 96
Population-wide distributions of neural activity during perceptual decision-making
Wohrer, A., Humphries, M. D., & Machens, C. K. (2012) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.09.004 · Cited by 96
Quantitative fMRI and oxidative neuroenergetics
Hyder, F., & Rothman, D. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.027 · Cited by 88
Dynamic relationships between spontaneous and evoked electrophysiological activity
Wainio‐Theberge, S., Wolff, A., & Northoff, G. (2021) · Communications Biology · doi:10.1038/s42003-021-02240-9 · Cited by 101
Neuronal oscillations as a mechanistic substrate of auditory temporal prediction
Morillon, B., & Schroeder, C. E. (2015) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12629 · Cited by 93
Bakhurin, K. I., Mac, V., Golshani, P., & Masmanidis, S. C. (2016) · Journal of Neurophysiology · doi:10.1152/jn.01037.2015 · Cited by 76
Elementary visual hallucinations and their relationships to neural pattern-forming mechanisms.
Billock, V. A., & Tsou, B. H.-P. (2012) · Psychological Bulletin · doi:10.1037/a0027580 · Cited by 76
O’Donnell, C., Gonçalves, J. T., Portera‐Cailliau, C., & B, T. J. S. A. (2017) · eLife · doi:10.7554/elife.26724 · Cited by 69
Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas
Avitan, L., & Stringer, C. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.019 · Cited by 71
Population Rate Dynamics and Multineuron Firing Patterns in Sensory Cortex
Okun, M., Yger, P., Marguet, S. L., Gérard-Mercier, F., Benucci, A., Katzner, S., Busse, L., Carandini, M., & Harris, K. D. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1831-12.2012 · Cited by 75
Robustness of Traveling Waves in Ongoing Activity of Visual Cortex
Nauhaus, I., Busse, L., Ringach, D. L., & Carandini, M. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5827-11.2012 · Cited by 63
Self-citationA dual role of prestimulus spontaneous neural activity in visual object recognition
Podvalny, E., Flounders, M. W., King, L. E., Holroyd, T., & He, B. J. (2019) · Nature Communications · doi:10.1038/s41467-019-11877-4 · Cited by 69
Three brain states in the hippocampus and cortex
Kay, K., & Frank, L. M. (2018) · Hippocampus · doi:10.1002/hipo.22956 · Cited by 69
Spontaneous Neural Dynamics and Multi-scale Network Organization
Foster, B. L., He, B. J., Honey, C. J., Jerbi, K., Maier, A., & Saalmann, Y. B. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00007 · Cited by 63
Windows into stress: a glimpse at emerging roles for CRH PVN neurons
Rasiah, N. P., Loewen, S. P., & Bains, J. S. (2023) · Physiological Reviews · doi:10.1152/physrev.00056.2021 · Cited by 62
Hartmann, C., Lazăr, A., Nessler, B., & Triesch, J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004640 · Cited by 61
Mesoscale Architecture Shapes Initiation and Richness of Spontaneous Network Activity
Okujeni, S., Kandler, S., & Egert, U. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2552-16.2017 · Cited by 61
Self-Induced Switchings between Multiple Space-Time Patterns on Complex Networks of Excitable Units
Ansmann, G., Lehnertz, K., & Feudel, U. (2016) · Physical Review X · doi:10.1103/physrevx.6.011030 · Cited by 60
Saggar, M., Shine, J. M., Liégeois, R., Dosenbach, N. U. F., & Fair, D. A. (2022) · Nature Communications · doi:10.1038/s41467-022-32381-2 · Cited by 65
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Zheng, J., & Meister, M. (2024) · Neuron · doi:10.1016/j.neuron.2024.11.008 · Cited by 73
Function of inhibition in visual cortical processing
Alitto, H. J., & Dan, Y. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.02.012 · Cited by 57
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Camera, G. L., Fontanini, A., & Mazzucato, L. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.06.007 · Cited by 64
Reactivation of the Same Synapses during Spontaneous Up States and Sensory Stimuli
Chen, X., Rochefort, N. L., Sakmann, B. J., & Konnerth, A. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.05.042 · Cited by 58
Bistability of prefrontal states gates access to consciousness
Dwarakanath, A., Kapoor, V., Werner, J., Safavi, S., Fedorov, L., Logothetis, N. K., & Panagiotaropoulos, T. I. (2023) · Neuron · doi:10.1016/j.neuron.2023.02.027 · Cited by 63
Nonlinear decoding of a complex movie from the mammalian retina
Botella‐Soler, V., Deny, S., Martius, G., Marre, O., & Tkačik, G. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006057 · Cited by 52
Müllenbroich, M. C., Turrini, L., Silvestri, L., Alterini, T., Gheisari, A., Tiso, N., Vanzi, F., Sacconi, L., & Pavone, F. S. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00315 · Cited by 50
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Velázquez, J. L. P., & Galán, R. F. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00037 · Cited by 61
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Giulioni, M., Camilleri, P., Mattia, M., Dante, V., Braun, J., & Giudice, P. D. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00149 · Cited by 50
Dissociation of task engagement and arousal effects in auditory cortex and midbrain
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Scarpetta, S., & CANDIA, A. D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064162 · Cited by 47
Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy
Li, Z., Liu, H., Liao, X., Xu, J., Liu, W., Tian, F., Yong He, A., & Niu, H. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.002337 · Cited by 47
Balanced neural architecture and the idling brain
Doiron, B., & Litwin-Kumar, A. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00056 · Cited by 44
Local and thalamic origins of correlated ongoing and sensory-evoked cortical activities
Malina, K. C.-K., Mohar, B., Rappaport, A. N., & Lampl, I. (2016) · Nature Communications · doi:10.1038/ncomms12740 · Cited by 43
A statistical method for analyzing and comparing spatiotemporal cortical activation patterns
Krauß, P., Metzner, C., Schilling, A., Tziridis, K., Traxdorf, M., Wollbrink, A., Rampp, S., Pantev, C., & Schulze, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-23765-w · Cited by 46
Zhao, J. W., Wang, D., & Wang, J. (2012) · Molecular Brain · doi:10.1186/1756-6606-5-12 · Cited by 44
Suppression of cortical neural variability is stimulus- and state-dependent
White, B. M., Abbott, L. F., & Fiser, J. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00723.2011 · Cited by 43
Alain Destexhe, A. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.06.002 · Cited by 47
Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex
Hutchison, R. M., Hashemi, N., Gati, J. S., Menon, R. S., & Everling, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.062 · Cited by 43
Corticothalamic Spike Transfer via the L5B-POm Pathway in vivo
Mease, R. A., Sumser, A., Sakmann, B. J., & Groh, A. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw123 · Cited by 40
Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes
Yu, Z., Liu, J. K., Jia, S., Zhang, Y., Zheng, Y., Tian, Y., & Huang, T. (2020) · Engineering · doi:10.1016/j.eng.2020.02.004 · Cited by 43
Synaptic patterning and the timescales of cortical dynamics
Duarte, R., Seeholzer, A., Zilles, K., & Morrison, A. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.02.007 · Cited by 38
Rapid and active stabilization of visual cortical firing rates across light–dark transitions
Pacheco, A. T., Tilden, E. I., Grutzner, S. M., Lane, B. J., Wu, Y. K., Hengen, K. B., Gjorgjieva, J., & Turrigiano, G. G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906595116 · Cited by 37
Neocortical activity is stimulus- and scale-invariant
Karimipanah, Y., Ma, Z., Miller, J.-E. K., Yuste, R., & Weßel, R. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177396 · Cited by 34
Chen, G., & Gong, P. (2019) · Nature Communications · doi:10.1038/s41467-019-12918-8 · Cited by 37
Emergence of spontaneous assembly activity in developing neural networks without afferent input
Triplett, M. A., Avitan, L., & Goodhill, G. J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006421 · Cited by 39
Griffis, J. C., Elkhetali, A. S., Burge, W. K., Chen, R. H., & Visscher, K. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00338 · Cited by 36
Donner, T. H., Sagi, D., Bonneh, Y. S., & Heeger, D. J. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3388-12.2013 · Cited by 36
Mitchell, D. E., Kwan, A., Carriot, J., Chacron, M. J., & Cullen, K. E. (2018) · eLife · doi:10.7554/elife.43019 · Cited by 33
Tuning dissimilarity explains short distance decline of spontaneous spike correlation in macaque V1
Chu, C. C. J., Chien, P. F., & Hung, C. P. (2014) · Vision Research · doi:10.1016/j.visres.2014.01.008 · Cited by 36
Belief states as a framework to explain extra-retinal influences in visual cortex
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Real-Time Functional MRI in the Treatment of Mental Health Disorders
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Exact estimation of multiple directed acyclic graphs
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Strong and weak principles of neural dimension reduction
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Spontaneous and evoked activity patterns diverge over development
Avitan, L., Pujic, Z., Mölter, J., Zhu, S., Sun, B., & Goodhill, G. J. (2021) · eLife · doi:10.7554/elife.61942 · Cited by 27
Hancock, F., Rosas, F. E., Mediano, P. A. M., Luppi, A. I., Cabral, J., Dipasquale, O., & Turkheimer, F. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0214 · Cited by 26
Marachlian, E., Avitan, L., Goodhill, G. J., & Sumbre, G. (2018) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00046 · Cited by 29
Bayesian inference of neuronal assemblies
Diana, G., Sainsbury, T. T. J., & Meyer, M. P. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007481 · Cited by 33
Afrashteh, N., Inayat, S., Bermudez-Contreras, E. J., Luczak, A., McNaughton, B. L., & Mohajerani, M. H. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110081 · Cited by 29
No Man is an Island: Social Coordination and the Environment
Nyborg, K. (2019) · Environmental and Resource Economics · doi:10.1007/s10640-020-00415-2 · Cited by 30
Martinez, D., Metzen, M. G., & Chacron, M. J. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00594.2016 · Cited by 28
Distributed representation of context by intrinsic subnetworks in prefrontal cortex
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Metadata
- Authors
- Ringach, D. L.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Ringach, Dario L
- ORCID
- Ringach, Dario L
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2009-08
- Volume
- 19
- Issue
- 4
- Pages
- 439-444
- ISSN
- 0959-4388 (ISSN-L)
- PMC
- PMC3319344
- OpenAlex
- W2148439693
- Cited by
- 234
- References
- 50
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 11 · 2023: 29 · 2022: 14 · 2021: 21 · 2020: 8 · 2019: 13 · 2018: 14 · 2017: 18 · 2016: 15 · 2015: 17 · 2014: 7 · 2013: 13 · 2012: 16
- FWCI
- 0.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Opinion in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Eye Institute (R01EY018322, EY-12816, EY18322, R01EY012816)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- spontaneous neuronal activity, cortical activity, cortical connectivity, spatiotemporal structure, top-down prediction, expectations, cortical computation, noise
- MeSH terms
- Animals, Cerebral Cortex, Models, Neurological, Nerve Net, Neurons, Photic Stimulation, Visual Perception, Neural Networks, Computer
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