Cognition as a Window into Neuronal Population Space
Open accessAbstract
Understanding how cognitive processes affect the responses of sensory neurons may clarify the relationship between neuronal population activity and behavior. However, tools for analyzing neuronal activity have not kept up with technological advances in recording from large neuronal populations. Here, we describe prevalent hypotheses of how cognitive processes affect sensory neurons, driven largely by a model based on the activity of single neurons or pools of neurons as the units of computation. We then use simple simulations to expand this model to a new conceptual framework that focuses on subspaces of population activity as the relevant units of computation, uses comparisons between brain areas or to behavior to guide analyses of these subspaces, and suggests that population activity is optimized to decode the large variety of stimuli and tasks that animals encounter in natural behavior. This framework provides new ways of understanding the ever-growing quantity of recorded population activity data.
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Neural Mechanisms of Selective Visual Attention
Desimone, R., & Duncan, J. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001205 · Cited by 8,388 · see citing works
Humans integrate visual and haptic information in a statistically optimal fashion
Ernst, M. O., & Banks, M. S. (2002) · Nature · doi:10.1038/415429a · Cited by 4,844 · see citing works
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764 · see citing works
Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention
Fries, P., Reynolds, J. H., Rorie, A. E., & Desimone, R. (2001) · Science · doi:10.1126/science.1055465 · Cited by 2,786 · see citing works
Rhythms for cognition: communication through coherence
Fries, P. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.034 · Cited by 2,725 · see citing works
Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices
Buschman, T. J., & Miller, E. K. (2007) · Science · doi:10.1126/science.1138071 · Cited by 2,380 · 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
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403112111 · Cited by 1,924 · see citing works
How does the brain solve visual object recognition?
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.010 · Cited by 1,790 · see citing works
Neural correlations, population coding and computation
Averbeck, B. B., Latham, P. E., & Pouget, A. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1888 · Cited by 1,743 · see citing works
Entrainment of Neuronal Oscillations as a Mechanism of Attentional Selection
Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I., & Schroeder, C. E. (2008) · Science · doi:10.1126/science.1154735 · Cited by 1,719 · see citing works
Neural population dynamics during reaching
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012) · Nature · doi:10.1038/nature11129 · Cited by 1,636 · see citing works
Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621 · 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
Dimensionality reduction for large-scale neural recordings
Cunningham, J. P., & Yu, B. M. (2014) · Nature Neuroscience · doi:10.1038/nn.3776 · Cited by 1,219 · see citing works
Deep supervised, but not unsupervised, models may explain IT cortical representation
Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204 · see citing works
Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4
McAdams, C. J., & Maunsell, J. H. R. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-01-00431.1999 · Cited by 1,201 · see citing works
High-frequency, long-range coupling between prefrontal and visual cortex during attention
Gregoriou, G. G., Gotts, S. J., Zhou, H., & Desimone, R. (2009) · Science · doi:10.1126/science.1171402 · Cited by 1,194 · see citing works
Attentional modulation of visual processing
Reynolds, J. H., & Chelazzi, L. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131039 · Cited by 1,158 · see citing works
Selective gating of visual signals by microstimulation of frontal cortex
Moore, T., & Armstrong, K. M. (2003) · Nature · doi:10.1038/nature01341 · Cited by 1,156 · 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
Self-citationA 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 · see citing works
STRUCTURE AND FUNCTION OF VISUAL AREA MT
Born, R. T., & Bradley, D. C. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131052 · Cited by 1,023 · cited 1× · see citing works
“For the direction discrimination task, it may seem straightforward for the brain to divide neurons into appropriate pools based on their direction tuning, a feature which is neatly organized in columns in MT (Albright & Desimone 1987, Born & Bradley 2005).”
BackgroundMeasuring and interpreting neuronal correlations
Cohen, M. R., & Kohn, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2842 · Cited by 1,016 · see citing works
Self-citationFeature-based attention in visual cortex
Maunsell, J. H. R., & Treue, S. (2006) · Trends in Neurosciences · doi:10.1016/j.tins.2006.04.001 · Cited by 960 · see citing works
Untangling invariant object recognition
DiCarlo, J. J., & Cox, D. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.06.010 · Cited by 948 · see citing works
Brain States: Top-Down Influences in Sensory Processing
Gilbert, C. D., & Sigman, M. (2007) · Neuron · doi:10.1016/j.neuron.2007.05.019 · Cited by 936 · see citing works
The neural systems that mediate human perceptual decision making
Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008) · Nature reviews. Neuroscience · doi:10.1038/nrn2374 · Cited by 877 · see citing works
Bayesian decision theory in sensorimotor control
Kording, K. P., & Wolpert, D. M. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.003 · Cited by 866 · see citing works
The Effect of Correlated Variability on the Accuracy of a Population Code
Abbott, L. F., & Dayan, P. (1999) · Neural Computation · doi:10.1162/089976699300016827 · Cited by 833 · see citing works
Cortical activity in the null space: permitting preparation without movement
Kaufman, M. T., Churchland, M. M., Ryu, S. I., & Shenoy, K. V. (2014) · Nature Neuroscience · doi:10.1038/nn.3643 · Cited by 831 · see citing works
Shadlen, M., Britten, K., Newsome, W., & Movshon, J. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-04-01486.1996 · Cited by 824 · see citing works
Cadieu, C. F., Hong, H., Yamins, D. L. K., Pinto, N., Ardila, D., Solomon, E. A., Majaj, N. J., & DiCarlo, J. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003963 · Cited by 824 · see citing works
SENSE AND THE SINGLE NEURON: Probing the Physiology of Perception
Parker, A. J., & Newsome, W. T. (1998) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.21.1.227 · Cited by 801 · see citing works
Extracting information from neuronal populations: information theory and decoding approaches
Quiroga, R. Q., & Panzeri, S. (2009) · Nature reviews. Neuroscience · doi:10.1038/nrn2578 · Cited by 799 · see citing works
From the neuron doctrine to neural networks
Yuste, R. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3962 · Cited by 748 · see citing works
Gamma-band synchronization in visual cortex predicts speed of change detection
Womelsdorf, T., Fries, P., Mitra, P. P., & Desimone, R. (2005) · Nature · doi:10.1038/nature04258 · Cited by 741 · see citing works
Attentional stimulus selection through selective synchronization between monkey visual areas
Bosman, C. A., Schoffelen, J.-M., Brunet, N., Oostenveld, R., Bastos, A. M., Womelsdorf, T., Rubehn, B., Stieglitz, T., De Weerd, P., & Fries, P. (2012) · Neuron · doi:10.1016/j.neuron.2012.06.037 · Cited by 720 · see citing works
Neural constraints on learning
Sadtler, P. T., Quick, K. M., Golub, M. D., Chase, S. M., Ryu, S. I., Tyler-Kabara, E. C., Yu, B. M., & Batista, A. P. (2014) · Nature · doi:10.1038/nature13665 · Cited by 713 · see citing works
Spatial 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 · see citing works
Differential attention-dependent response modulation across cell classes in macaque visual area V4
Mitchell, J. F., Sundberg, K. A., & Reynolds, J. H. (2007) · Neuron · doi:10.1016/j.neuron.2007.06.018 · Cited by 665 · see citing works
Parallel and Serial Neural Mechanisms for Visual Search in Macaque Area V4
Bichot, N. P., Rossi, A. F., & Desimone, R. (2005) · Science · doi:10.1126/science.1109676 · Cited by 651 · 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
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
Partitioning neuronal variability
Goris, R. L. T., Movshon, J. A., & Simoncelli, E. P. (2014) · Nature Neuroscience · doi:10.1038/nn.3711 · Cited by 528 · see citing works
Stimulus Dependence of Neuronal Correlation in Primary Visual Cortex of the Macaque
Kohn, A., & Smith, M. A. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.5106-04.2005 · Cited by 521 · see citing works
Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area
Law, C.-T., & Gold, J. I. (2008) · Nature Neuroscience · doi:10.1038/nn2070 · Cited by 516 · see citing works
Correlated Firing in Macaque Visual Area MT: Time Scales and Relationship to Behavior
Bair, W., Zohary, E., & Newsome, W. T. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-05-01676.2001 · Cited by 515 · see citing works
The role of neuronal synchronization in selective attention
Womelsdorf, T., & Fries, P. (2007) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2007.02.002 · Cited by 511 · see citing works
Reorganization between preparatory and movement population responses in motor cortex
Elsayed, G. F., Lara, A. H., Kaufman, M. T., Churchland, M. M., & Cunningham, J. P. (2016) · Nature Communications · doi:10.1038/ncomms13239 · Cited by 465 · see citing works
Neural correlates of reliability-based cue weighting during multisensory integration
Fetsch, C. R., Pouget, A., DeAngelis, G. C., & Angelaki, D. E. (2012) · Nature Neuroscience · doi:10.1038/nn.2983 · Cited by 459 · see citing works
Neural Mechanisms of Visual Attention: How Top-Down Feedback Highlights Relevant Locations
Saalmann, Y. B., Pigarev, I. N., & Vidyasagar, T. R. (2007) · Science · doi:10.1126/science.1139140 · Cited by 419 · see citing works
Decision-related activity in sensory neurons reflects more than a neuron’s causal effect
Nienborg, H., & Cumming, B. G. (2009) · Nature · doi:10.1038/nature07821 · Cited by 360 · 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
Cortical mechanisms of space-based and object-based attentional control
Yantis, S., & Serences, J. T. (2003) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(03)00033-3 · Cited by 342 · see citing works
Neuronal Mechanisms of Visual Attention
Maunsell, J. H. R. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035431 · Cited by 332 · see citing works
Optimal integration of texture and motion cues to depth
Jacobs, R. A. (1999) · Vision Research · doi:10.1016/s0042-6989(99)00088-7 · Cited by 324 · see citing works
Probing perceptual decisions in rodents
Carandini, M., & Churchland, A. K. (2013) · Nature Neuroscience · doi:10.1038/nn.3410 · Cited by 323 · see citing works
Coding of Horizontal Disparity and Velocity by MT Neurons in the Alert Macaque
DeAngelis, G. C., & Uka, T. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00717.2002 · Cited by 278 · see citing works
Attentional enhancement of spatial resolution: linking behavioural and neurophysiological evidence
Anton-Erxleben, K., & Carrasco, M. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3443 · Cited by 277 · see citing works
Local precision of visuotopic organization in the middle temporal area (MT) of the macaque
Albright, T. D., & Desimone, R. (1987) · Experimental Brain Research · doi:10.1007/bf00235981 · Cited by 277 · see citing works
Cortical circuits for the control of attention
Miller, E. K., & Buschman, T. J. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.11.011 · Cited by 261 · see citing works
Reversible large-scale modification of cortical networks during neuroprosthetic control
Ganguly, K., Dimitrov, D. F., Wallis, J. D., & Carmena, J. M. (2011) · Nature Neuroscience · doi:10.1038/nn.2797 · Cited by 256 · see citing works
Context-Dependent Changes in Functional Circuitry in Visual Area MT
Cohen, M. R., & Newsome, W. T. (2008) · Neuron · doi:10.1016/j.neuron.2008.08.007 · Cited by 252 · see citing works
Self-citationAttention deficits without cortical neuronal deficits
Zénon, A., & Krauzlis, R. J. (2012) · Nature · doi:10.1038/nature11497 · Cited by 234 · see citing works
Navigating the Neural Space in Search of the Neural Code
Jazayeri, M., & Afraz, A. (2017) · Neuron · doi:10.1016/j.neuron.2017.02.019 · Cited by 227 · see citing works
Perceptual Learning Reduces Interneuronal Correlations in Macaque Visual Cortex
Gu, Y., Liu, S., Fetsch, C. R., Yang, Y., Fok, S., Sunkara, A., DeAngelis, G. C., & Angelaki, D. E. (2011) · Neuron · doi:10.1016/j.neuron.2011.06.015 · Cited by 218 · see citing works
Attention enhances synaptic efficacy and the signal-to-noise ratio in neural circuits
Briggs, F., Mangun, G. R., & Usrey, W. M. (2013) · Nature · doi:10.1038/nature12276 · Cited by 217 · see citing works
Cohen, M. R., & Newsome, W. T. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5179-08.2009 · Cited by 214 · see citing works
Self-citationUsing 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 214 · see citing works
Self-citationThe role of attention in visual processing
Maunsell, J. H. R., & Cook, E. P. (2002) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2002.1107 · Cited by 214 · see citing works
Learning to See the Difference Specifically Alters the Most Informative V4 Neurons
Raiguel, S., Vogels, R., Mysore, S. G., & Orban, G. A. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.0457-06.2006 · Cited by 200 · see citing works
Decision-Related Activity in Sensory Neurons: Correlations Among Neurons and with Behavior
Nienborg, H., Cohen, M. R., & Cumming, B. G. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150403 · Cited by 199 · see citing works
Self-citationAttention 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
Self-citationKnill, D. C. (2007) · Journal of Vision · doi:10.1167/7.7.5 · Cited by 191 · see citing works
Attention can either increase or decrease spike count correlations in visual cortex
Ruff, D. A., & Cohen, M. R. (2014) · Nature Neuroscience · doi:10.1038/nn.3835 · Cited by 188 · see citing works
Self-citationLearning 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
Self-citationNeural underpinnings of the evidence accumulator
Brody, C. D., & Hanks, T. D. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.01.003 · Cited by 184 · see citing works
Wimmer, K., Compte, A., Roxin, A., Peixoto, D., Renart, A., & de la Rocha, J. (2015) · Nature Communications · doi:10.1038/ncomms7177 · Cited by 182 · see citing works
Lesions of prefrontal cortex reduce attentional modulation of neuronal responses and synchrony in V4
Gregoriou, G. G., Rossi, A. F., Ungerleider, L. G., & Desimone, R. (2014) · Nature Neuroscience · doi:10.1038/nn.3742 · Cited by 177 · see citing works
Behaviorally Selective Engagement of Short-Latency Effector Pathways by Motor Cortex
Miri, A., Warriner, C. L., Seely, J., Elsayed, G. F., Cunningham, J. P., Churchland, M. M., & Jessell, T. M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.042 · Cited by 175 · 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
Perceptual training continuously refines neuronal population codes in primary visual cortex
Yan, Y., Rasch, M. J., Chen, M., Xiang, X., Huang, M., Wu, S., & Li, W. (2014) · Nature Neuroscience · doi:10.1038/nn.3805 · Cited by 157 · see citing works
Laminar Organization of Attentional Modulation in Macaque Visual Area V4
Nandy, A. S., Nassi, J. J., & Reynolds, J. H. (2016) · Neuron · doi:10.1016/j.neuron.2016.11.029 · Cited by 155 · see citing works
Herrero, J. L., Gieselmann, M. A., Sanayei, M., & Thiele, A. (2013) · Neuron · doi:10.1016/j.neuron.2013.03.029 · Cited by 151 · see citing works
Structure in neural population recordings: an expected byproduct of simpler phenomena?
Elsayed, G. F., & Cunningham, J. P. (2017) · Nature Neuroscience · doi:10.1038/nn.4617 · Cited by 143 · see citing works
Klink, P. C., Dagnino, B., Gariel-Mathis, M.-A., & Roelfsema, P. R. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.033 · Cited by 135 · see citing works
Fine Discrimination Training Alters the Causal Contribution of Macaque Area MT to Depth Perception
Chowdhury, S. A., & DeAngelis, G. C. (2008) · Neuron · doi:10.1016/j.neuron.2008.08.023 · Cited by 134 · see citing works
Optimal multisensory decision-making in a reaction-time task
Drugowitsch, J., DeAngelis, G. C., Klier, E. M., Angelaki, D. E., & Pouget, A. (2014) · eLife · doi:10.7554/elife.03005 · Cited by 133 · see citing works
How Can Single Sensory Neurons Predict Behavior?
Pitkow, X., Liu, S., Angelaki, D. E., DeAngelis, G. C., & Pouget, A. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.033 · Cited by 129 · see citing works
Signals in inferotemporal and perirhinal cortex suggest an untangling of visual target information
Pagan, M., Urban, L. S., Wohl, M. P., & Rust, N. C. (2013) · Nature Neuroscience · doi:10.1038/nn.3433 · Cited by 125 · see citing works
Huang, X., & Lisberger, S. G. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00010.2009 · Cited by 124 · see citing works
Associative Learning Enhances Population Coding by Inverting Interneuronal Correlation Patterns
Jeanne, J. M., Sharpee, T. O., & Gentner, T. Q. (2013) · Neuron · doi:10.1016/j.neuron.2013.02.023 · Cited by 122 · see citing works
Spatial and Temporal Scales of Neuronal Correlation in Visual Area V4
Smith, M. A., & Sommer, M. A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4782-12.2013 · Cited by 120 · see citing works
Luo, T. Z., & Maunsell, J. H. R. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.007 · Cited by 111 · see citing works
The Contribution of Area MT to Visual Motion Perception Depends on Training
Liu, L. D., & Pack, C. C. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.024 · Cited by 108 · see citing works
Computational principles and models of multisensory integration
Chandrasekaran, C. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.11.002 · Cited by 102 · see citing works
Effects of Task Difficulty and Target Likelihood in Area V4 of Macaque Monkeys
Boudreau, C. E., Williford, T. H., & Maunsell, J. H. R. (2006) · Journal of Neurophysiology · doi:10.1152/jn.01072.2005 · Cited by 98 · see citing works
Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control
Golub, M. D., Chase, S. M., Batista, A. P., & Yu, B. M. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.12.005 · Cited by 98 · see citing works
Relating population-code representations between man, monkey, and computational models
Kriegeskorte, N. (2009) · Frontiers in Neuroscience · doi:10.3389/neuro.01.035.2009 · 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
Attention Increases Spike Count Correlations between Visual Cortical Areas
Ruff, D. A., & Cohen, M. R. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0610-16.2016 · Cited by 84 · see citing works
Self-citationPoort, J., Self, M. W., van Vugt, B., Malkki, H. A. I., & Roelfsema, P. R. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw235 · Cited by 84 · 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
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
Self-citationVisual 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
Inference in the Brain: Statistics Flowing in Redundant Population Codes
Pitkow, X., & Angelaki, D. E. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.028 · Cited by 73 · see citing works
Simultaneous selection by object-based attention in visual and frontal cortex
Pooresmaeili, A., Poort, J., & Roelfsema, P. R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1316181111 · Cited by 72 · see citing works
Measuring Fisher Information Accurately in Correlated Neural Populations
Kanitscheider, I., Coen-Cagli, R., Kohn, A., & Pouget, A. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004218 · Cited by 70 · see citing works
Stimulus Dependence of Correlated Variability across Cortical Areas
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Self-citationJoint tuning for direction of motion and binocular disparity in macaque MT is largely separable
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Flagged first, then by citations
Research works printed but not linked: 1
- Ni AM, Ruff DA, Alberts JJ, Symmonds J, Cohen MR 2017 Learning and attention reveal a general relationship between neuronal variability and perception. bioRxiv 137083. 10.1101/137083 PMC6571104 29371470 · DOI
Cited by
Sorted by citations
Neuromodulation of brain state and behavior
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Mechanisms underlying gain modulation in the cortex
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- Authors
- Ruff, D. A., Ni, A. M., & Cohen, M. R.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Ruff, Douglas A.
- Ni, Amy M.
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2018-07-08
- Volume
- 41
- Issue
- 1
- Pages
- 77-97
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- PMC
- PMC6571103
- OpenAlex
- W2803360267
- Cited by
- 63
- References
- 123
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 12 · 2023: 8 · 2022: 9 · 2021: 8 · 2020: 6 · 2019: 3 · 2018: 2 · 2016: 1
- FWCI
- 0.64
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual Review of Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Eye Institute (R01EY022930, R00EY020844, P30EY008098)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- neural population activity, sensory neurons, cognitive processes, single neuron model, population coding, brain-behavior relationship, computational neuroscience
- MeSH terms
- Action Potentials, Afferent Pathways, Cerebral Cortex, Cognition, Computer Simulation, Humans, Models, Neurological, Perception, Sensory Receptor Cells
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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