Dimensionality reduction for large-scale neural recordings
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Most sensory, cognitive and motor functions depend on the interactions of many neurons. In recent years, there has been rapid development and increasing use of technologies for recording from large numbers of neurons, either sequentially or simultaneously. A key question is what scientific insight can be gained by studying a population of recorded neurons beyond studying each neuron individually. Here, we examine three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets. Many recent studies have adopted dimensionality reduction to analyze these populations and to find features that are not apparent at the level of individual neurons. We describe the dimensionality reduction methods commonly applied to population activity and offer practical advice about selecting methods and interpreting their outputs. This review is intended for experimental and computational researchers who seek to understand the role dimensionality reduction has had and can have in systems neuroscience, and who seek to apply these methods to their own data.
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Analysis of intrinsic dimensionality confirms high-dimensional neural coding during epoch 1 that collapses rapidly to just a single dimension following first touch during epochs 2, 3, and 4.
“Analysis of intrinsic dimensionality (44) (Materials and Methods) during decision-making (Fig. 2M) confirms the high-dimensional neural coding during epoch 1 that collapses rapidly to just a single dimension following the FT during epochs 2, 3, and 4.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [44]
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(2005) · Springer eBooks · doi:10.1007/0-387-27255-0_9 · Cited by 20,333 · see citing works
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965 · see citing works
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585 · see citing works
Neuronal Population Coding of Movement Direction
Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986) · Science · doi:10.1126/science.3749885 · Cited by 3,187 · see citing works
Subspace identification for linear systems: theory, implementation, applications
Overschee, P. V., & Moor, B. L. R. D. (1996) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,271 · see citing works
Context-dependent computation by recurrent dynamics in prefrontal cortex
Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013) · Nature · doi:10.1038/nature12742 · Cited by 1,807 · see citing works
Weak pairwise correlations imply strongly correlated network states in a neural population
Schneidman, E., Berry, M. J., Segev, R., & Bialek, W. (2006) · Nature · doi:10.1038/nature04701 · Cited by 1,701 · see citing works
The importance of mixed selectivity in complex cognitive tasks
Rigotti, M., Barak, O., Warden, M. R., Wang, X.-J., Daw, N. D., Miller, E. K., & Fusi, S. (2013) · Nature · doi:10.1038/nature12160 · Cited by 1,639 · 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
Self-citationWhole-brain functional imaging at cellular resolution using light-sheet microscopy
Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M., & Keller, P. J. (2013) · Nature Methods · doi:10.1038/nmeth.2434 · Cited by 1,392 · 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
Coding of Odors by a Receptor Repertoire
Hallem, E. A., & Carlson, J. R. (2006) · Cell · doi:10.1016/j.cell.2006.01.050 · Cited by 1,265 · see citing works
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 · 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
Self-citationTruccolo, 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
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 982 · see citing works
Choice-specific sequences in parietal cortex during a virtual-navigation decision task
Harvey, C. D., Coen, P., & Tank, D. W. (2012) · Nature · doi:10.1038/nature10918 · Cited by 916 · see citing works
A Unifying Review of Linear Gaussian Models
Roweis, S. T., & Zoubin Ghahramani, A. (1999) · Neural Computation · doi:10.1162/089976699300016674 · Cited by 873 · see citing works
Multiple neural spike train data analysis: state-of-the-art and future challenges
Brown, E. N., Kass, R. E., & Mitra, P. P. (2004) · Nature Neuroscience · doi:10.1038/nn1228 · Cited by 857 · see citing works
Fast Readout of Object Identity from Macaque Inferior Temporal Cortex
Hung, C. P., Kreiman, G., Poggio, T., & DiCarlo, J. J. (2005) · Science · doi:10.1126/science.1117593 · Cited by 854 · see citing works
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 835 · 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
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
Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment
Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011) · Science · doi:10.1126/science.1195870 · Cited by 782 · see citing works
Olfactory network dynamics and the coding of multidimensional signals
Laurent, G. (2002) · Nature reviews. Neuroscience · doi:10.1038/nrn964 · Cited by 733 · see citing works
Brain-wide neuronal dynamics during motor adaptation in zebrafish
Ahrens, M. B., Li, J. M., Orger, M. B., Robson, D. N., Schier, A. F., Engert, F., & Portugues, R. (2012) · Nature · doi:10.1038/nature11057 · Cited by 729 · 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
Self-citationDynamic Coding for Cognitive Control in Prefrontal Cortex
Stokes, M. G., Kusunoki, M., Sigala, N., Nili, H., Gaffan, D., & Duncan, J. (2013) · Neuron · doi:10.1016/j.neuron.2013.01.039 · Cited by 711 · see citing works
Functional organization of human sensorimotor cortex for speech articulation
Bouchard, K. E., Mesgarani, N., Johnson, K., & Chang, E. F. (2013) · Nature · doi:10.1038/nature11911 · Cited by 686 · see citing works
Yu, B. M., Cunningham, J. P., Santhanam, G., Ryu, S. I., Shenoy, K. V., & Sahani, M. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90941.2008 · Cited by 659 · see citing works
Self-citationIntensity versus identity coding in an olfactory system.
Stopfer, M., Jayaraman, V., & Laurent, G. (2003) · Neuron · doi:10.1016/j.neuron.2003.08.011 · Cited by 627 · see citing works
Spontaneous Events Outline the Realm of Possible Sensory Responses in Neocortical Populations
Luczak, A., Barthó, P., & Harris, K. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.014 · Cited by 593 · see citing works
Brown, E. N., Frank, L. M., Tang, D., Quirk, M. C., & Wilson, M. A. (1998) · Journal of Neuroscience · doi:10.1523/jneurosci.18-18-07411.1998 · Cited by 575 · see citing works
A high-performance neural prosthesis enabled by control algorithm design
Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., Churchland, M. M., Kaufman, M. T., Kao, J. C., Ryu, S. I., et al. (2012) · Nature Neuroscience · doi:10.1038/nn.3265 · Cited by 564 · see citing works
Self-citationNicolelis, M. A. L., Baccalá, L. A., Lin, R. C. S., & Chapin, J. K. (1995) · Science · doi:10.1126/science.7761855 · Cited by 548 · see citing works
Asymptotics of Graphical Projection Pursuit
Diaconis, P. W., & Freedman, D. H. (1984) · The Annals of Statistics · doi:10.1214/aos/1176346703 · Cited by 541 · see citing works
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 506 · see citing works
Self-citationVogels, T. P., Rajan, K., & Abbott, L. F. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135637 · Cited by 497 · see citing works
Vector reconstruction from firing rates
Salinas, E., & Abbott, L. F. (1994) · Journal of Computational Neuroscience · doi:10.1007/bf00962720 · Cited by 485 · see citing works
Mazor, O., & Laurent, G. (2005) · Neuron · doi:10.1016/j.neuron.2005.09.032 · Cited by 484 · see citing works
The Structure of Multi-Neuron Firing Patterns in Primate Retina
Shlens, J., Field, G. D., Gauthier, J. L., Grivich, M. I., Petrusca, D., Sher, A., Litke, A. M., & Chichilnisky, E. J. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.1282-06.2006 · Cited by 471 · see citing works
Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter
Wu, W. L., Gao, Y., Bienenstock, E., Donoghue, J. P., & Black, M. J. (2005) · Neural Computation · doi:10.1162/089976606774841585 · Cited by 460 · see citing works
Kumar, A., Rotter, S., & Aertsen, A. (2010) · Nature reviews. Neuroscience · doi:10.1038/nrn2886 · Cited by 448 · see citing works
Simultaneously Recorded Trains of Action Potentials: Analysis and Functional Interpretation
Gerstein, G. L., & Perkel, D. H. (1969) · Science · doi:10.1126/science.164.3881.828 · Cited by 417 · see citing works
Estimating a State-Space Model from Point Process Observations
Smith, A. C., & Brown, E. N. (2003) · Neural Computation · doi:10.1162/089976603765202622 · Cited by 395 · see citing works
Durstewitz, D., Vittoz, N. M., Floresco, S., & Seamans, J. K. (2010) · Neuron · doi:10.1016/j.neuron.2010.03.029 · Cited by 351 · see citing works
Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering
Eden, U. T., Frank, L. M., Barbieri, R., Solo, V., & Brown, E. N. (2004) · Neural Computation · doi:10.1162/089976604773135069 · Cited by 351 · see citing works
Imaging in vivo: watching the brain in action
Kerr, J. N. D., & Denk, W. T. (2008) · Nature reviews. Neuroscience · doi:10.1038/nrn2338 · Cited by 344 · see citing works
Dynamics of Population Code for Working Memory in the Prefrontal Cortex
Baeg, E. H., Kim, Y. B., Huh, K., Mook-Jung, I., Kim, H. T., & Jung, M. W. (2003) · Neuron · doi:10.1016/s0896-6273(03)00597-x · Cited by 338 · see citing works
Optical Imaging of Neuronal Populations During Decision-Making
Briggman, K. L., Abarbanel, H. D. I., & Kristan, W. B. (2005) · Science · doi:10.1126/science.1103736 · Cited by 337 · 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
Mapping brain activity at scale with cluster computing
Freeman, J., Vladimirov, N., Kawashima, T., Mu, Y., Sofroniew, N. J., Bennett, D. V., Rosen, J., Yang, C.-T., Looger, L. L., & Ahrens, M. B. (2014) · Nature Methods · doi:10.1038/nmeth.3041 · Cited by 299 · see citing works
Cortical activity flips among quasi-stationary states.
Abeles, M., Bergman, H., Gat, I., Meilijson, I., Seidemann, E., Tishby, N., & Vaadia, E. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.19.8616 · Cited by 295 · see citing works
Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles
Jones, L. M., Fontanini, A., Sadacca, B. F., Miller, P. C., & Katz, D. B. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0705546104 · Cited by 287 · see citing works
Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities
Kipke, D. R., Shain, W. G., Buzsáki, G., Fetz, E. E., Henderson, J. M., Hetke, J. F., & Schalk, G. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.3879-08.2008 · Cited by 283 · see citing works
Functional, But Not Anatomical, Separation of “What” and “When” in Prefrontal Cortex
Machens, C. K., Romo, R., & Brody, C. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.3276-09.2010 · Cited by 260 · see citing works
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 257 · see citing works
Self-citationBathellier, B., Buhl, D. L., Accolla, R., & Carleton, A. (2008) · Neuron · doi:10.1016/j.neuron.2008.02.011 · Cited by 254 · see citing works
Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus
Stanley, G. B., Li, F. F., & Dan, Y. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-18-08036.1999 · Cited by 240 · see citing works
A new look at state-space models for neural data
Paninski, L., Ahmadian, Y., Ferreira, D. G., Koyama, S., Rad, K. R., Vidne, M., Vogelstein, J. T., & Wu, W. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0179-x · Cited by 221 · see citing works
Measurement of variability dynamics in cortical spike trains
Nawrot, M. P., Boucsein, C., Molina, V., Alexa Riehle, C., Aertsen, A., & Rotter, S. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.10.013 · Cited by 198 · see citing works
Encoding and Decoding of Overlapping Odor Sequences
Broome, B. M., Jayaraman, V., & Laurent, G. (2006) · Neuron · doi:10.1016/j.neuron.2006.07.018 · Cited by 192 · see citing works
Encoding Network States by Striatal Cell Assemblies
Carrillo‐Reid, L., Tecuapetla, F., Tapia, D., Hernández‐Cruz, A., Galarraga, E., Drucker-Colı́n, R., & Bargas, J. (2008) · Journal of Neurophysiology · doi:10.1152/jn.01131.2007 · Cited by 187 · see citing works
Metastability of Active CA3 Networks
Sasaki, T., Matsuki, N., & Ikegaya, Y. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.4514-06.2007 · Cited by 180 · see citing works
Encoding a temporally structured stimulus with a temporally structured neural representation
Brown, S. L., Joseph, J., & Stopfer, M. (2005) · Nature Neuroscience · doi:10.1038/nn1559 · Cited by 172 · see citing works
Empirical models of spiking in neural populations
Macke, J. H., Buesing, L., Cunningham, J. P., Yu, B. M., Shenoy, K. V., & Sahani, M. (2011) · Max Planck Digital Library · Cited by 172 · see citing works
Self-citationA Neuronal Population Measure of Attention Predicts Behavioral Performance on Individual Trials
Cohen, M. R., & Maunsell, J. H. R. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2171-10.2010 · Cited by 171 · see citing works
Neural Dynamics of Reaching following Incorrect or Absent Motor Preparation
Ames, K. C., Ryu, S. I., & Shenoy, K. V. (2014) · Neuron · doi:10.1016/j.neuron.2013.11.003 · Cited by 170 · see citing works
Horwitz, G. D., & Newsome, W. T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.5.2543 · Cited by 168 · see citing works
Seidemann, E., Meilijson, I., Abeles, M., Bergman, H., & Vaadia, E. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-02-00752.1996 · Cited by 165 · see citing works
Detecting Neural-State Transitions Using Hidden Markov Models for Motor Cortical Prostheses
Kemere, C., Santhanam, G., Yu, B. M., Afshar, A., Ryu, S. I., Meng, T. H., & Shenoy, K. V. (2008) · Journal of Neurophysiology · doi:10.1152/jn.00924.2007 · Cited by 157 · see citing works
Self-citationDelay activity in rodent frontal cortex during a simple reaction time task.
Narayanan, N. S., & Laubach, M. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90615.2008 · Cited by 138 · see citing works
Shimazaki, H., Амари, Ш.-И., Brown, E. N., & Grün, S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002385 · Cited by 136 · see citing works
Lin, L., Osan, R. M., Shoham, S., Jin, W., Zuo, W., & Tsien, J. Z. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0408233102 · Cited by 117 · see citing works
Factor-Analysis Methods for Higher-Performance Neural Prostheses
Santhanam, G., Yu, B. M., Gilja, V., Ryu, S. I., Afshar, A., Sahani, M., & Shenoy, K. V. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00097.2009 · Cited by 117 · see citing works
Self-citationNeural Encoding of Rapidly Fluctuating Odors
Geffen, M. N., Broome, B. M., Laurent, G., & Meister, M. (2009) · Neuron · doi:10.1016/j.neuron.2009.01.021 · Cited by 116 · see citing works
Common-input models for multiple neural spike-train data
Kulkarni, J. E., & Paninski, L. (2007) · Network Computation in Neural Systems · doi:10.1080/09548980701625173 · Cited by 114 · 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
Raman, B., Joseph, J., Tang, J., & Stopfer, M. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.5639-09.2010 · Cited by 110 · see citing works
A spatiotemporal coding mechanism for background-invariant odor recognition
Saha, D., Leong, K., Li, C., Peterson, S. M., Siegel, G., & Raman, B. (2013) · Nature Neuroscience · doi:10.1038/nn.3570 · Cited by 109 · see citing works
Heterogenous Population Coding of a Short-Term Memory and Decision Task
Jun, J. K., Miller, P. C., Hernández, A., Zainos, A., Lemus, L., Brody, C. D., & Romo, R. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2062-09.2010 · Cited by 96 · see citing works
Population decoding of motor cortical activity using a generalized linear model with hidden states
Lawhern, V. J., Wu, W. L., Hatsopoulos, N. G., & Paninski, L. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.03.024 · Cited by 92 · see citing works
Neural dynamics of choice: Single-trial analysis of decision-related activity in parietal cortex
Bollimunta, A., Totten, D., & Ditterich, J. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5752-11.2012 · Cited by 83 · see citing works
Methods for estimating neural firing rates, and their application to brain–machine interfaces
Cunningham, J. P., Gilja, V., Ryu, S. I., & Shenoy, K. V. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.02.004 · Cited by 83 · see citing works
Self-citationExtracting Dynamical Structure Embedded in Neural Activity
Yu, B. M., Afshar, A., Santhanam, G., Ryu, S. I., Shenoy, K. V., & Sahani, M. (2005) · Cited by 70 · see citing works
Self-citationPaz, R., Natan, C., Boraud, T., Bergman, H., & Vaadia, E. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.0164-05.2005 · Cited by 65 · see citing works
Dynamics of cortical neuronal ensembles transit from decision making to storage for later report
Ponce-Alvarez, A., Nácher, V., Luna, R., Riehle, A., & Romo, R. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.6176-11.2012 · Cited by 59 · see citing works
Demixed Principal Component Analysis
Brendel, W., Romo, R., & Machens, C. K. (2011) · Cited by 55 · see citing works
Cowley, B. R., Kaufman, M. T., Butler, Z. S., Churchland, M. M., Ryu, S. I., Shenoy, K. V., & Yu, B. M. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/6/066012 · Cited by 54 · see citing works
Self-citationDynamical segmentation of single trials from population neural data
Petresкa, B., Yu, B. M., Cunningham, J. P., Santhanam, G., Ryu, S. I., Shenoy, K. V., & Sahani, M. (2011) · UCL Discovery (University College London) · Cited by 54 · see citing works
Self-citationTwo layers of neural variability
Churchland, M., & Abbott, L. (2012) · Nature Neuroscience · doi:10.1038/nn.3247 · Cited by 52 · see citing works
Population coding of tone stimuli in auditory cortex: dynamic rate vector analysis
Barthó, P., Curto, C., Luczak, A., Marguet, S. L., & Harris, K. D. (2009) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2009.06954.x · Cited by 46 · see citing works
The Role of Sensory Network Dynamics in Generating a Motor Program
Levi, R., Varona, P., Arshavsky, Y. I., Rabinovich, M. I., & Selverston, A. I. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.2249-05.2005 · Cited by 46 · see citing works
Robust learning of low-dimensional dynamics from large neural ensembles
Pfau, D., Pnevmatikakis, E. A., & Paninski, L. (2013) · Cited by 39 · see citing works
Two-Manifold ProblemsPreprint
Boots, B., & Gordon, G. J. (2011) · arXiv (Cornell University) · doi:10.1184/r1/6476423 · Cited by 37 · see citing works
Crouching tiger, hidden dimensions
Sanger, T. D., & Kalaska, J. F. (2014) · Nature Neuroscience · doi:10.1038/nn.3663 · Cited by 6 · see citing works
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Research works printed but not linked: 2
- [69]Danoczy M Hahnloser R Efficient estimation of hidden state dynamics from spike trains Adv. Neural Inf. Process. Syst 2006 8 227 234
- [75]Buesing L Macke J Sahani M Spectral learning of linear dynamics from generalized-linear observations with application to neural population data Adv. Neural Inf. Process. Syst 2012 25 1691 1699
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Feature dimensionality reduction: a review
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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
Self-citationNeural 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 · see citing works
Computation Through Neural Population Dynamics
Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092619-094115 · Cited by 671 · see citing works
Demixed principal component analysis of neural population data
Kobak, D., Brendel, W., Constantinidis, C., Feierstein, C. E., Kepecs, Á., Mainen, Z. F., Qi, X.-L., Romo, R., Uchida, N., & Machens, C. K. (2016) · eLife · doi:10.7554/elife.10989 · Cited by 602 · 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
Global Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans
Kato, S., Kaplan, H. S., Schrödel, T., Skora, S., Lindsay, T. H., Yemini, E., Lockery, S. R., & Zimmer, M. (2015) · Cell · doi:10.1016/j.cell.2015.09.034 · Cited by 523 · see citing works
Human cognition involves the dynamic integration of neural activity and neuromodulatory systems
Shine, J. M., Breakspear, M. J., Bell, P. T., Martens, K. A. E., Shine, R., Koyejo, S., Sporns, O., & Poldrack, R. A. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0312-0 · Cited by 518 · see citing works
Brunton, B. W., Johnson, L. A., Ojemann, J. G., & Kutz, J. N. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.10.010 · Cited by 472 · 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
Self-citationLinear Dimensionality Reduction: Survey, Insights, and GeneralizationsPreprint
Cunningham, J. P., & Zoubin Ghahramani, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.0873 · Cited by 462 · see citing works
Self-citationComputational Neuroethology: A Call to Action
Datta, S. R., Anderson, D. J., Branson, K., Perona, P., & Leifer, A. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.038 · Cited by 453 · see citing works
Genetic Dissection of Neural Circuits: A Decade of Progress
Luo, L., Callaway, E. M., & Svoboda, K. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.040 · Cited by 446 · see citing works
Flexible timing by temporal scaling of cortical responses
Wang, J., Narain, D., Hosseini, E. A., & Jazayeri, M. (2017) · Nature Neuroscience · doi:10.1038/s41593-017-0028-6 · Cited by 425 · see citing works
Distinct descending motor cortex pathways and their roles in movement
Economo, M. N., Viswanathan, S., Tasic, B., Bas, E., Winnubst, J., Menon, V., Graybuck, L. T., Nguyen, T. N., Smith, K. A., Yao, Z., et al. (2018) · Nature · doi:10.1038/s41586-018-0642-9 · Cited by 415 · see citing works
On simplicity and complexity in the brave new world of large-scale neuroscience
Gao, P., & Ganguli, S. (2015) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.04.003 · Cited by 406 · see citing works
The role of prefrontal cortex in working memory: a mini review
Lara, A. H., & Wallis, J. D. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00173 · Cited by 402 · see citing works
Long-term stability of cortical population dynamics underlying consistent behavior
Gallego, J. Á., Perich, M. G., Chowdhury, R. H., Solla, S. A., & Miller, L. E. (2020) · Nature Neuroscience · doi:10.1038/s41593-019-0555-4 · Cited by 398 · see citing works
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 394 · see citing works
Perceptual Decision Making in Rodents, Monkeys, and Humans
Hanks, T. D., & Summerfield, C. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.003 · Cited by 388 · see citing works
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 383 · see citing works
Self-citationCorrelated neural activity and encoding of behavior across brains of socially interacting animals
Kingsbury, L., Huang, S., Wang, J., Gu, K., Golshani, P., Wu, Y. E., & Hong, W. (2019) · Cell · doi:10.1016/j.cell.2019.05.022 · Cited by 380 · see citing works
Cortical 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 337 · see citing works
A theory of multineuronal dimensionality, dynamics and measurement
Gao, P., Trautmann, E., Yu, B., Santhanam, G., Ryu, S., Shenoy, K., & Ganguli, S. (2017) · doi:10.1101/214262 · Cited by 332 · see citing works
Self-citationCould a neuroscientist understand a microprocessor?
Jonas, E., & Paul Kording, K. (2016) · doi:10.1101/055624 · Cited by 329 · see citing works
Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics
Adam, Y., Kim, J. J., Lou, S., Zhao, Y., Xie, M. E., Brinks, D., Wu, H., Mostajo-Radji, M. A., Kheifets, S., Parot, V., et al. (2019) · Nature · doi:10.1038/s41586-019-1166-7 · Cited by 327 · 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
Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires
Sainburg, T., Thielk, M., & Gentner, T. Q. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008228 · Cited by 324 · see citing works
Towards the neural population doctrine
Saxena, S., & Cunningham, J. P. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.02.002 · Cited by 317 · see citing works
Self-citationWilliams, 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
Corticoamygdala Transfer of Socially Derived Information Gates Observational Learning
Allsop, S. A., Wichmann, R., Mills, F., Burgos-Robles, A., Chang, C.-J., Felix-Ortiz, A. C., Vienne, A., Beyeler, A., Izadmehr, E. M., Glober, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.04.004 · Cited by 288 · see citing works
Levin, M. G., Pezzulo, G., & Finkelstein, J. M. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071114-040647 · Cited by 287 · see citing works
Zebrafish Behavior: Opportunities and Challenges
Orger, M. B., & de Polavieja, G. G. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-033857 · Cited by 285 · see citing works
Song, H. F., Yang, G. R., & Wang, X.-J. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004792 · Cited by 281 · see citing works
A dedicated network for social interaction processing in the primate brain
Sliwa, J., & Freiwald, W. A. (2017) · Science · doi:10.1126/science.aam6383 · Cited by 281 · see citing works
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 274 · see citing works
Self-citationThe population doctrine in cognitive neuroscience
Ebitz, R. B., & Hayden, B. Y. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.011 · Cited by 273 · see citing works
Training deep neural density estimators to identify mechanistic models of neural dynamics
Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., Chintaluri, C., Podlaski, W. F., Haddad, S. A., Vogels, T. P., et al. (2020) · eLife · doi:10.7554/elife.56261 · Cited by 252 · see citing works
A Tutorial for Information Theory in Neuroscience
Timme, N. M., & Lapish, C. C. (2018) · eNeuro · doi:10.1523/eneuro.0052-18.2018 · Cited by 241 · see citing works
The role of population structure in computations through neural dynamics
Dubreuil, A., Valente, A., Beiran, M., Mastrogiuseppe, F., & Ostojic, S. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01088-4 · Cited by 237 · 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
Causes and consequences of representational drift
Rule, M. E., O’Leary, T., & Harvey, C. D. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.08.005 · Cited by 227 · see citing works
Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior
Panzeri, S., Harvey, C. D., Piasini, E., Latham, P. E., & Fellin, T. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.036 · Cited by 227 · 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
Re-evaluating Circuit Mechanisms Underlying Pattern Separation
Cayco-Gajic, N. A., & Silver, R. A. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.044 · Cited by 220 · see citing works
Neural population coding: combining insights from microscopic and mass signals
Panzeri, S., Macke, J. H., Groß, J., & Kayser, C. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.01.002 · Cited by 219 · see citing works
Impact of anxiety on prefrontal cortex encoding of cognitive flexibility
Park, J., & Moghaddam, B. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2016.06.013 · Cited by 218 · see citing works
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 · see citing works
Degenhart, A. D., Bishop, W. E., Oby, E. R., Tyler-Kabara, E. C., Chase, S. M., Batista, A. P., & Yu, B. M. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0542-9 · Cited by 212 · see citing works
Self-citationA unifying perspective on neural manifolds and circuits for cognition
Langdon, C., Genkin, M., & Engel, T. A. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00693-x · Cited by 210 · see citing works
A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement
Inagaki, H., Chen, S., Ridder, M. C., Sah, P., Li, N., Yang, Z., Hasanbegović, H., Gao, Z., Gerfen, C. R., & Svoboda, K. (2022) · Cell · doi:10.1016/j.cell.2022.02.006 · Cited by 207 · see citing works
Whole-central nervous system functional imaging in larval Drosophila
Lemon, W. C., Pulver, S. R., Höckendorf, B., McDole, K., Branson, K. M., Freeman, J., & Keller, P. (2015) · Nature Communications · doi:10.1038/ncomms8924 · Cited by 199 · 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
Jazayeri, M., & Ostojic, S. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.08.002 · Cited by 193 · see citing works
A global brain state underlies C. elegans sleep behavior
Nichols, A. L. A., Eichler, T., Latham, R., & Zimmer, M. (2017) · Science · doi:10.1126/science.aam6851 · Cited by 188 · see citing works
Volumetric Ca2+ Imaging in the Mouse Brain Using Hybrid Multiplexed Sculpted Light Microscopy
Weisenburger, S., Tejera, F., Demas, J., Chen, B., Manley, J., Sparks, F. T., Traub, F. M., Daigle, T. L., Zeng, H., Losonczy, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.011 · Cited by 188 · see citing works
Single-trial dynamics of motor cortex and their applications to brain-machine interfaces
Kao, J. C., Nuyujukian, P., Ryu, S. I., Churchland, M. M., Cunningham, J. P., & Shenoy, K. V. (2015) · Nature Communications · doi:10.1038/ncomms8759 · Cited by 184 · see citing works
Self-citationA Neural Population Mechanism for Rapid Learning
Perich, M. G., Gallego, J. Á., & Miller, L. E. (2018) · Neuron · doi:10.1016/j.neuron.2018.09.030 · Cited by 184 · see citing works
The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states
Munn, B., Müller, E. J., Wainstein, G., & Shine, J. M. (2021) · Nature Communications · doi:10.1038/s41467-021-26268-x · Cited by 177 · see citing works
Cheung, V. C. K., Cheung, B. M. F., Zhang, J. H., Chan, Z. Y. S., Ha, S. C. W., Chen, C., & Cheung, R. T. H. (2020) · Nature Communications · doi:10.1038/s41467-020-18210-4 · Cited by 175 · see citing works
A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology
Wei, Z., Lin, B., Chen, T., Daie, K., Svoboda, K., & Druckmann, S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008198 · Cited by 173 · see citing works
Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making
Aoi, M., Mante, V., & Pillow, J. W. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0696-5 · Cited by 173 · see citing works
Perceptual restoration of masked speech in human cortex
Leonard, M. K., Baud, M. O., Sjerps, M. J., & Chang, E. F. (2016) · Nature Communications · doi:10.1038/ncomms13619 · Cited by 170 · see citing works
Natural behavior is the language of the brain
Miller, C. T., Gire, D. H., Hoke, K. L., Huk, A. C., Kelley, D. B., Leopold, D. A., Smear, M. C., Theunissen, F. E., Yartsev, M. M., & Niell, C. M. (2022) · Current Biology · doi:10.1016/j.cub.2022.03.031 · Cited by 170 · see citing works
NetPyNE, a tool for data-driven multiscale modeling of brain circuits
Dura-Bernal, S., Suter, B. A., Gleeson, P., Cantarelli, M., Quintana, A., Rodriguez, F., Kedziora, D. J., Chadderdon, G. L., Kerr, C. C., Neymotin, S. A., et al. (2019) · eLife · doi:10.7554/elife.44494 · Cited by 169 · see citing works
Probing neural codes with two-photon holographic optogenetics
Adesnik, H., & Abdeladim, L. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00902-9 · Cited by 168 · see citing works
Broken detailed balance and entropy production in the human brain
Lynn, C. W., Cornblath, E. J., Papadopoulos, L., Bertolero, M. A., & Bassett, D. S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109889118 · Cited by 166 · see citing works
Peeling the Onion of Brain Representations
Kriegeskorte, N., & Diedrichsen, J. (2019) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-080317-061906 · Cited by 160 · see citing works
Approaches to revealing the neural basis of muscle synergies: a review and a critique
Cheung, V. C. K., & Seki, K. (2021) · Journal of Neurophysiology · doi:10.1152/jn.00625.2019 · Cited by 158 · see citing works
Cortical ensembles orchestrate social competition through hypothalamic outputs
Padilla-Coreano, N., Batra, K., Patarino, M., Chen, Z., Rock, R. R., Zhang, R., Hausmann, S. B., Weddington, J., Patel, R. R., Zhang, Y. E., et al. (2022) · Nature · doi:10.1038/s41586-022-04507-5 · Cited by 157 · see citing works
The roles of supervised machine learning in systems neuroscience
Glaser, J. I., Benjamin, A. S., Farhoodi, R., & Kording, K. P. (2019) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2019.01.008 · Cited by 156 · see citing works
Review of bio-optical imaging systems with a high space-bandwidth product
Park, J., Brady, D. J., Zheng, G., Tian, L., & Gao, L. (2021) · Advanced Photonics · doi:10.1117/1.ap.3.4.044001 · Cited by 151 · see citing works
Luan, L., Robinson, J. T., Aazhang, B., Chi, T., Yang, K., Li, X., Rathore, H., Singer, A., Yellapantula, S., Fan, Y., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.10.011 · Cited by 150 · see citing works
Yu, H., Zeng, F., Liu, D., Wang, J., & Liu, J. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3530922 · Cited by 150 · see citing works
Reconstructing neuronal circuitry from parallel spike trains
Kobayashi, R., Kurita, S., Kurth, A. C., Kitano, K., Mizuseki, K., Diesmann, M., Richmond, B. J., & Shinomoto, S. (2019) · Nature Communications · doi:10.1038/s41467-019-12225-2 · Cited by 149 · see citing works
Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification
Sani, O. G., Abbaspourazad, H., Wong, Y. T., Pesaran, B., & Shanechi, M. M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00733-0 · Cited by 149 · see citing works
Big data and the industrialization of neuroscience: A safe roadmap for understanding the brain?
Frégnac, Y. (2017) · Science · doi:10.1126/science.aan8866 · Cited by 147 · see citing works
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 146 · see citing works
Circuit Models of Low-Dimensional Shared Variability in Cortical Networks
Huang, C., Ruff, D. A., Pyle, R., Rosenbaum, R., Cohen, M. R., & Doiron, B. (2018) · Neuron · doi:10.1016/j.neuron.2018.11.034 · Cited by 144 · see citing works
Dopamine facilitates associative memory encoding in the entorhinal cortex
Lee, J. Y., Jun, H., Soma, S., Nakazono, T., Shiraiwa, K., Dasgupta, A., Nakagawa, T., Xie, J. L., Chavez, J., Romo, R., et al. (2021) · Nature · doi:10.1038/s41586-021-03948-8 · Cited by 144 · 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
Self-citationPrefrontal-Periaqueductal Gray-Projecting Neurons Mediate Context Fear Discrimination
Rozeske, R. R., Jercog, D., Karalis, N., Chaudun, F., Khoder, S., Girard, D., Winke, N., & Herry, C. (2018) · Neuron · doi:10.1016/j.neuron.2017.12.044 · Cited by 143 · see citing works
Manifold Learning: What, How, and Why
Meilă, M., & Zhang, H. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-040522-115238 · Cited by 141 · see citing works
The brain in motion: How ensemble fluidity drives memory-updating and flexibility
Mau, W., Hasselmo, M. E., & Cai, D. J. (2020) · eLife · doi:10.7554/elife.63550 · Cited by 139 · see citing works
Optimal anticipatory control as a theory of motor preparation: A thalamo-cortical circuit model
Kao, T.-C., Sadabadi, M. S., & Hennequin, G. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.009 · Cited by 139 · see citing works
Neural Population Dynamics Underlying Motor Learning Transfer
Vyas, S., Even-Chen, N., Stavisky, S. D., Ryu, S. I., Nuyujukian, P., & Shenoy, K. V. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.040 · Cited by 138 · see citing works
Revealing neural correlates of behavior without behavioral measurements
Rubin, A., Sheintuch, L., Brande-Eilat, N., Pinchasof, O., Rechavi, Y., Geva, N., & Ziv, Y. (2019) · Nature Communications · doi:10.1038/s41467-019-12724-2 · Cited by 137 · see citing works
Discovering causal relations and equations from data
Camps‐Valls, G., Gerhardus, A., Ninad, U., Varando, G., Martius, G., Balaguer‐Ballester, E., Vinuesa, R., Díaz, E., Zanna, L., & Runge, J. (2023) · Physics Reports · doi:10.1016/j.physrep.2023.10.005 · Cited by 136 · see citing works
Endogenous Sequential Cortical Activity Evoked by Visual Stimuli
Carrillo‐Reid, L., Miller, J.-E. K., Hamm, J. P., Jackson, J., & Yuste, R. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5214-14.2015 · Cited by 134 · see citing works
Population coding of valence in the basolateral amygdala
Zhang, X., & Li, B. (2018) · Nature Communications · doi:10.1038/s41467-018-07679-9 · Cited by 133 · see citing works
Optogenetics: 10 years after ChR2 in neurons—views from the community
Adamantidis, A., Arber, S., Bains, J. S., Bamberg, E., Bonci, A., Buzsáki, G., Cardin, J. A., Costa, R. M., Dan, Y., Goda, Y., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4106 · Cited by 130 · 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
Self-citationGenetically identified amygdala–striatal circuits for valence-specific behaviors
Zhang, X., Guan, W., Yang, T., Furlan, A., Xiao, X., Yu, K., An, X., Galbavy, W., Ramakrishnan, C., Deisseroth, K., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00927-0 · Cited by 126 · see citing works
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 · see citing works
Preparatory activity and the expansive null-space
Churchland, M. M., & Shenoy, K. V. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00796-z · Cited by 126 · see citing works
Intrinsic neuronal dynamics predict distinct functional roles during working memory
Wasmuht, D., Spaak, E., Buschman, T. J., Miller, E. K., & Stokes, M. G. (2018) · Nature Communications · doi:10.1038/s41467-018-05961-4 · Cited by 124 · see citing works
Carnevale, F., de Lafuente, V., Romo, R., Barak, O., & Parga, N. (2015) · Neuron · doi:10.1016/j.neuron.2015.04.014 · Cited by 119 · see citing works
- Authors
- Cunningham, J. P., & Yu, B. M.
- Affiliations
- Columbia University (US)
- Center for the Neural Basis of Cognition (US)
- Corresponding author
- Cunningham, John P
- Yu, Byron M
- Venue
- Nature Neuroscience
- Published
- 2014-08-24
- Volume
- 17
- Issue
- 11
- Pages
- 1500-1509
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC4433019
- OpenAlex
- W2017539895
- Cited by
- 1,219
- References
- 98
- Citations by year
- 2026: 141 · 2025: 137 · 2024: 135 · 2023: 116 · 2022: 123 · 2021: 115 · 2020: 85 · 2019: 103 · 2018: 82 · 2017: 82 · 2016: 50 · 2015: 41 · 2014: 9
- FWCI
- 5.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD071686)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Undergraduate Neuroscience Education and Research
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- dimensionality reduction, neural population activity, large-scale neural recording, population coding, single-trial analysis, statistical power, exploratory data analysis, systems neuroscience, method selection
- MeSH terms
- Animals, Data Collection, Humans, Nerve Net, Neurons, Statistics as Topic
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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