The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
Dimensionality reduction for large-scale neural recordings
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(2005) · Springer eBooks · doi:10.1007/0-387-27255-0_9 · Cited by 20,333
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
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
Neuronal Population Coding of Movement Direction
Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986) · Science · doi:10.1126/science.3749885 · Cited by 3,219
Subspace Identification for Linear Systems
Overschee, P. V., & Moor, B. D. (1996) · Springer eBooks · doi:10.1007/978-1-4613-0465-4 · Cited by 2,593
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
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 1,910
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 1,745
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 1,704
Self-citationThe 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
Whole-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
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 1,332
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 1,286
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
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
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 1,078
Measuring and interpreting neuronal correlations
Cohen, M. R., & Kohn, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2842 · Cited by 1,059
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
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 951
A Unifying Review of Linear Gaussian Models
Roweis, S. T., & Ghahramani, Z. (1999) · Neural Computation · doi:10.1162/089976699300016674 · Cited by 884
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 866
Cortical Control of Arm Movements: A Dynamical Systems Perspective
Shenoy, K. V., Sahani, M., & Churchland, M. M. (2013) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150509 · Cited by 865
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
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
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 827
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
Olfactory network dynamics and the coding of multidimensional signals
Laurent, G. (2002) · Nature reviews. Neuroscience · doi:10.1038/nrn964 · Cited by 738
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 · cited 2×
“A telling example comes from optical recordings throughout the entire brain of a larval zebrafish at cellular resolution during motor adaptation 50 .”
BackgroundNeural 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
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
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
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 678
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 614
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
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 585
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
Self-citationNicolelis, M. A. L., Baccalá, L. A., Lin, R. C. S., & Chapin, J. K. (1995) · Science · doi:10.1126/science.7761855 · Cited by 556
Asymptotics of Graphical Projection Pursuit
Diaconis, P. W., & Freedman, D. H. (1984) · The Annals of Statistics · doi:10.1214/aos/1176346703 · Cited by 541
Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine?
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Ryu, S. I., & Shenoy, K. V. (2010) · Neuron · doi:10.1016/j.neuron.2010.09.015 · Cited by 526
Self-citationVogels, T. P., Rajan, K., & Abbott, L. F. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135637 · Cited by 497
Mazor, O., & Laurent, G. (2005) · Neuron · doi:10.1016/j.neuron.2005.09.032 · Cited by 495
Vector reconstruction from firing rates
Salinas, E., & Abbott, L. F. (1994) · Journal of Computational Neuroscience · doi:10.1007/bf00962720 · Cited by 488
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 469
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 465
Kumar, A., Rotter, S., & Aertsen, A. (2010) · Nature reviews. Neuroscience · doi:10.1038/nrn2886 · Cited by 446
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 415
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
Durstewitz, D., Vittoz, N. M., Floresco, S., & Seamans, J. K. (2010) · Neuron · doi:10.1016/j.neuron.2010.03.029 · Cited by 369
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 356
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 353
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 341
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 338
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
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
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 295
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
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 294
Bathellier, B., Buhl, D. L., Accolla, R., & Carleton, A. (2008) · Neuron · doi:10.1016/j.neuron.2008.02.011 · Cited by 262
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
Single-Trial Neural Correlates of Arm Movement Preparation
Afshar, A., Santhanam, G., Yu, B. M., Ryu, S. I., Sahani, M., & Shenoy, K. V. (2011) · Neuron · doi:10.1016/j.neuron.2011.05.047 · Cited by 249
Self-citationReconstruction 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 244
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 224
Measurement of variability dynamics in cortical spike trains
Nawrot, M. P., Boucsein, C., Molina, V., Riehle, A., Aertsen, A., & Rotter, S. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.10.013 · Cited by 206
Encoding Network States by Striatal Cell Assemblies
Carrillo‐Reid, L., Tecuapetla, F., Tapia, D., Hernández‐Cruz, A., Galarraga, E., Drucker-Colín, R., & Bargas, J. (2008) · Journal of Neurophysiology · doi:10.1152/jn.01131.2007 · Cited by 194
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 189
A 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 181
Metastability of Active CA3 Networks
Sasaki, T., Matsuki, N., & Ikegaya, Y. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.4514-06.2007 · Cited by 178
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 172
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
Self-citationHorwitz, G. D., & Newsome, W. T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.5.2543 · Cited by 168
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 163
Seidemann, E., Meilijson, I., Abeles, M., Bergman, H., & Vaadia, E. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-02-00752.1996 · Cited by 162
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
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 144
Shimazaki, H., Амари, Ш.-И., Brown, E. N., & Grün, S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002385 · Cited by 132
Neural 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 121
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 118
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 114
Self-citationRaman, B., Joseph, J., Tang, J., & Stopfer, M. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.5639-09.2010 · Cited by 110
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 110
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 109
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 104
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 98
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 93
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 85
Self-citationNeural 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
Extracting 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
Self-citationPaz, R., Natan, C., Boraud, T., Bergman, H., & Vaadia, E. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.0164-05.2005 · Cited by 69
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
Demixed Principal Component Analysis
Brendel, W., Romo, R., & Machens, C. K. (2011) · Cited by 55
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
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
Self-citationTwo layers of neural variability
Churchland, M. M., & Abbott, L. F. (2012) · Nature Neuroscience · doi:10.1038/nn.3247 · Cited by 48
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 47
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 45
Robust learning of low-dimensional dynamics from large neural ensembles
Pfau, D., Pnevmatikakis, E. A., & Paninski, L. (2013) · Cited by 39
Two-Manifold ProblemsPreprint
Boots, B., & Gordon, G. J. (2011) · arXiv (Cornell University) · doi:10.1184/r1/6476423 · Cited by 37
Crouching tiger, hidden dimensions
Sanger, T. D., & Kalaska, J. F. (2014) · Nature Neuroscience · doi:10.1038/nn.3663 · Cited by 6
Research works printed but not linked: 2
- Danoczy, M. & Hahnloser, R. Efficient estimation of hidden state dynamics from spike trains. in Adv. Neural Inf. Process. Syst. 18, 227–234 (2006).
- 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. 25, 1691–1699 (2012).
Cited by
Sorted by citations
Bassett, D. S., & Sporns, O. (2017) · Nature Neuroscience · doi:10.1038/nn.4502 · Cited by 2,398
Computational psychiatry as a bridge from neuroscience to clinical applications
Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4238 · Cited by 1,002
Feature dimensionality reduction: a review
Jia, W., Sun, M., Lian, J., & Hou, S. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00637-x · Cited by 797
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
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
Metadata
- 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
- Social Sciences › Education › 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 ›
Are you an author, or do you see a problem with this page? Request a review
