Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping
Open accessPublished version of a preprint
Abstract
Though the temporal precision of neural computation has been studied intensively, a data-driven determination of this precision remains a fundamental challenge. Reproducible spike patterns may be obscured on single trials by uncontrolled temporal variability in behavior and cognition and may not be time locked to measurable signatures in behavior or local field potentials (LFP). To overcome these challenges, we describe a general-purpose time warping framework that reveals precise spike-time patterns in an unsupervised manner, even when these patterns are decoupled from behavior or are temporally stretched across single trials. We demonstrate this method across diverse systems: cued reaching in nonhuman primates, motor sequence production in rats, and olfaction in mice. This approach flexibly uncovers diverse dynamical firing patterns, including pulsatile responses to behavioral events, LFP-aligned oscillatory spiking, and even unanticipated patterns, such as 7 Hz oscillations in rat motor cortex that are not time locked to measured behaviors or LFP.
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
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?
After applying a time-alignment method to reduce temporal variability, the neural activity showed consistent patterns that were specific to each character.
“We used a time-alignment technique to remove temporal variability11, revealing remarkably consistent underlying patterns of neural activity that are unique to each character (Fig. 1c).”
From High-performance brain-to-text communication via handwriting (2021) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
(1994) · Knowledge Discovery and Data Mining · Cited by 3,137 · see citing works
Softky, W., & Koch, C. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00334.1993 · Cited by 1,689 · 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-citationDynamic Brain Sources of Visual Evoked Responses
Makeig, S., Westerfield, M., Jung, T.-P., Enghoff, S., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2002) · Science · doi:10.1126/science.1066168 · Cited by 1,630 · see citing works
Lam, S. K., Pitrou, A., & Seibert, S. (2015) · doi:10.1145/2833157.2833162 · Cited by 1,522 · see citing works
Wright, S. J. (2015) · Mathematical Programming · doi:10.1007/s10107-015-0892-3 · Cited by 1,438 · see citing works
Neuroscience Needs Behavior: Correcting a Reductionist Bias
Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., & Poeppel, D. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.041 · Cited by 1,358 · see citing works
A global averaging method for dynamic time warping, with applications to clustering
Petitjean, F., Ketterlin, A., & Gançarski, P. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.09.013 · Cited by 1,143 · see citing works
NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data
Pnevmatikakis, E. A., & Giovannucci, A. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.07.031 · Cited by 1,038 · see citing works
Coherent 25- to 35-Hz oscillations in the sensorimotor cortex of awake behaving monkeys.
Murthy, V. N., & Fetz, E. E. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.12.5670 · Cited by 996 · 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
Mapping of a non-spatial dimension by the hippocampal-entorhinal circuit
Aronov, D., Nevers, R., & Tank, D. W. (2017) · Nature · doi:10.1038/nature21692 · Cited by 773 · see citing works
Inferring single-trial neural population dynamics using sequential auto-encoders
Pandarinath, C., O’Shea, D. J., Collins, J., Józefowicz, R., Stavisky, S. D., Kao, J. C., Trautmann, E. M., Kaufman, M. T., Ryu, S. I., Hochberg, L. R., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0109-9 · Cited by 672 · see citing works
Self-citationDemixed 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
Oscillations in local field potentials of the primate motor cortex during voluntary movement.
Sanes, J. N., & Donoghue, J. P. (1993) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.90.10.4470 · Cited by 583 · see citing works
How advances in neural recording affect data analysis
Stevenson, I. H., & Kording, K. P. (2011) · Nature Neuroscience · doi:10.1038/nn.2731 · Cited by 564 · 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-citationMotor Cortex Is Required for Learning but Not for Executing a Motor Skill
Kawai, R., Markman, T. M., Poddar, R., Ko, R., Fantana, A. L., Dhawale, A. K., Kampff, A. R., & Ölveczky, B. P. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.024 · Cited by 551 · see citing works
Self-citationPartitioning neuronal variability
Goris, R. L. T., Movshon, J. A., & Simoncelli, E. P. (2014) · Nature Neuroscience · doi:10.1038/nn.3711 · Cited by 528 · see citing works
Fries, P., Roelfsema, P. R., Engel, A. K., Peter König, B., & Singer, W. (1997) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.94.23.12699 · Cited by 505 · see citing works
Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex
London, M., Roth, A., Beeren, L., Häusser, M., & Latham, P. (2010) · Nature · doi:10.1038/nature09086 · Cited by 494 · see citing works
Maximum likelihood estimation of cascade point-process neural encoding models
Paninski, L. (2004) · Network Computation in Neural Systems · doi:10.1088/0954-898x/15/4/002 · Cited by 483 · see citing works
Efficient codes and balanced networks
Denève, S., & Machens, C. K. (2016) · Nature Neuroscience · doi:10.1038/nn.4243 · Cited by 471 · see citing works
Yger, P., Spampinato, G. L., Esposito, E., Lefebvre, B., Deny, S., Gardella, C., Stimberg, M., Jetter, F., Zeck, G. M., Picaud, S. A., et al. (2018) · eLife · doi:10.7554/elife.34518 · Cited by 431 · 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
Precise olfactory responses tile the sniff cycle
Shusterman, R., Smear, M. C., Koulakov, A. A., & Rinberg, D. A. (2011) · Nature Neuroscience · doi:10.1038/nn.2877 · Cited by 391 · see citing works
Self-citationCross-validation of component models: A critical look at current methods
河野, 麻., Kjeldahl, K., Smilde, A. K., & Kiers, H. A. L. (2008) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-007-1790-1 · Cited by 341 · see citing works
Mechanisms of evoked and induced responses in MEG/EEG
Olivier David, A., Kilner, J. M., & Friston, K. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.034 · Cited by 339 · see citing works
Maximum likelihood estimation of cascade point-process neural encoding models
Paninski, L. (2004) · Network Computation in Neural Systems · doi:10.1088/0954-898x_15_4_002 · Cited by 307 · see citing works
Williams, 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
Self-citationNonparametric Regressin with Correlated Errors
Opsomer, J. D., Wang, Y., & Yang, Y. (2001) · Statistical Science · doi:10.1214/ss/1009213287 · Cited by 274 · see citing works
Neural antecedents of self-initiated actions in secondary motor cortex
Murakami, M., Vicente, M. I., Costa, G. M., & Mainen, Z. F. (2014) · Nature Neuroscience · doi:10.1038/nn.3826 · Cited by 272 · see citing works
Robust Odor Coding via Inhalation-Coupled Transient Activity in the Mammalian Olfactory Bulb
Cury, K. M., & Uchida, N. (2010) · Neuron · doi:10.1016/j.neuron.2010.09.040 · Cited by 271 · see citing works
Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain
Brette, R. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00151 · Cited by 264 · see citing works
Representation of Spatial Goals in Rat Orbitofrontal Cortex
Feierstein, C. E., Quirk, M. C., Uchida, N., Sosulski, D. L., & Mainen, Z. F. (2006) · Neuron · doi:10.1016/j.neuron.2006.06.032 · Cited by 248 · see citing works
The Largest Response Component in the Motor Cortex Reflects Movement Timing but Not Movement Type
Kaufman, M. T., Seely, J., Sussillo, D., Ryu, S. I., Shenoy, K. V., & Churchland, M. M. (2016) · eNeuro · doi:10.1523/eneuro.0085-16.2016 · Cited by 239 · see citing works
Self-citationA neural mechanism for sensing and reproducing a time interval
Jazayeri, M., & Shadlen, M. N. (2015) · Current Biology · doi:10.1016/j.cub.2015.08.038 · Cited by 210 · see citing works
Ensemble Coding of Vocal Control in Birdsong
Leonardo, A. M., & Fee, M. S. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.3036-04.2005 · Cited by 207 · see citing works
Dopamine reward prediction errors reflect hidden-state inference across time
Starkweather, C. K., Babayan, B. M., Uchida, N., & Gershman, S. J. (2017) · Nature Neuroscience · doi:10.1038/nn.4520 · Cited by 206 · see citing works
Interpretable whole-brain prediction analysis with GraphNet
Grosenick, L., Klingenberg, B., Katovich, K., Knutson, B. D., & Taylor, J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.12.062 · Cited by 197 · see citing works
A primacy code for odor identity
Wilson, C. D., Serrano, G. O., Koulakov, A. A., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01432-4 · Cited by 197 · see citing works
Self-citationBi-crossvalidation of the SVD and nonnegative matrix factorization
Owen, A. B., & Perry, P. O. (2013) · Cited by 188 · see citing works
Primary motor cortex reports efferent control of vibrissa motion on multiple time scales
Hill, D. N., Curtis, J., Moore, J. D., & Kleinfeld, D. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.020 · Cited by 187 · see citing works
Functional Data Analysis of Amplitude and Phase Variation
Marron, J. S., Ramsay, J. O., Sangalli, L. M., & Srivastava, A. (2015) · Statistical Science · doi:10.1214/15-sts524 · Cited by 176 · 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
Self-citationBi-cross-validation of the SVD and the nonnegative matrix factorization
Owen, A. B., & Perry, P. O. (2009) · The Annals of Applied Statistics · doi:10.1214/08-aoas227 · Cited by 163 · see citing works
Beta band oscillations in motor cortex reflect neural population signals that delay movement onset
Khanna, P., & Carmena, J. M. (2017) · eLife · doi:10.7554/elife.24573 · Cited by 160 · see citing works
Parallel detection of theta and respiration-coupled oscillations throughout the mouse brain
Tort, A. B. L., Ponsel, S., Jessberger, J., Yanovsky, Y., Brankačk, J., & Draguhn, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-24629-z · Cited by 159 · see citing works
Automated long-term recording and analysis of neural activity in behaving animals
Dhawale, A. K., Poddar, R., Wolff, S. B., Normand, V. A., Kopelowitz, E., & Ölveczky, B. P. (2017) · eLife · doi:10.7554/elife.27702 · Cited by 159 · see citing works
Self-citationNeural oscillations: beta band activity across motor networks
Khanna, P., & Carmena, J. M. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.11.010 · Cited by 134 · see citing works
Bruno, R. M. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.06.003 · Cited by 122 · see citing works
Mackevicius, E. L., Bahle, A. H., Williams, A. H., et al. (2019) · eLife · doi:10.7554/elife.38471 · Cited by 120 · see citing works
Self-citationDynamic Time Warping under limited warping path length
Zhang, Z., Tavenard, R., Bailly, A., et al. (2017) · Information Sciences · doi:10.1016/j.ins.2017.02.018 · Cited by 116 · see citing works
A Fully Automated High-Throughput Training System for Rodents
Poddar, R., Kawai, R., & Ölveczky, B. P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083171 · Cited by 112 · see citing works
Self-citationInferring hidden structure in multilayered neural circuits
Maheswaranathan, N., Kastner, D. B., Baccus, S. A., & Ganguli, S. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006291 · Cited by 66 · see citing works
Self-citationDetermination of Response Latency and Its Application to Normalization of Cross-Correlation Measures
Baker, S. N., & Gerstein, G. L. (2001) · Neural Computation · doi:10.1162/08997660152002889 · Cited by 51 · see citing works
Linear-nonlinear-time-warp-poisson models of neural activity
Lawlor, P. N., Perich, M. G., Miller, L. E., & Kording, K. P. (2018) · Journal of Computational Neuroscience · doi:10.1007/s10827-018-0696-6 · Cited by 51 · see citing works
Periodicity and Evoked Responses in Motor Cortex
Reimer, J., & Hatsopoulos, N. G. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.5947-09.2010 · Cited by 50 · see citing works
Aldworth, Z., Miller, J. P., Gedeon, T., et al. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.0359-05.2005 · Cited by 50 · see citing works
Chandrasekaran, C., Bray, I. E., & Shenoy, K. V. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1066-18.2018 · Cited by 47 · see citing works
Self-citationShushruth, S., Mazurek, M. E., & Shadlen, M. N. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0668-18.2018 · Cited by 45 · see citing works
Stimulus dependent diversity and stereotypy in the output of an olfactory functional unit
Arneodo, E. M., Penikis, K. B., Rabinowitz, N. C., Licata, A., Cichy, A., Zhang, J., Bozza, T. C., & Rinberg, D. A. (2018) · Nature Communications · doi:10.1038/s41467-018-03837-1 · Cited by 45 · see citing works
Self-citationAdvanced State Space Methods for Neural and Clinical Data
(2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781139941433 · Cited by 43 · see citing works
Temporal alignment and latent Gaussian process factor inference in population spike trainsPreprint
Duncker, L., & Sahani, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/331751 · Cited by 41 · see citing works
Estimating state and parameters in state space models of spike trains
Macke, J. H., Buesing, L., & Sahani, M. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781139941433.007 · Cited by 39 · see citing works
Trial time warping to discriminate stimulus-related from movement-related neural activity
Pérez, O., Kass, R. E., & Merchant, H. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.10.019 · Cited by 32 · see citing works
Shusterman, R., Sirotin, Y. B., Smear, M. C., Ahmadian, Y., & Rinberg, D. A. (2018) · eNeuro · doi:10.1523/eneuro.0149-18.2018 · Cited by 31 · see citing works
Self-citationEstimating receptive fields in the presence of spike-time jitter
Gollisch, T. (2006) · Network Computation in Neural Systems · doi:10.1080/09548980600569670 · Cited by 30 · see citing works
Testing for and Estimating Latency Effects for Poisson and Non-Poisson Spike Trains
Ventura, V. (2004) · Neural Computation · doi:10.1162/0899766041941952 · Cited by 21 · see citing works
Brittleness in model selection analysis of single neuron firing ratesPreprint
Chandrasekaran, C., Soldado-Magraner, J., Peixoto, D., Newsome, W. T., Shenoy, K. V., & Sahani, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/430710 · Cited by 20 · see citing works
Self-citationTrial-by-trial estimation of amplitude and latency variability in neuronal spike trains
Bollimunta, A., Knuth, K. H., & Ding, M. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.08.007 · Cited by 15 · see citing works
Linear-Nonlinear-Time-Warp-Poisson models of neural activityPreprint
Lawlor, P. N., Perich, M. G., Miller, L. E., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/194498 · Cited by 4 · see citing works
Computing loss of efficiency in optimal Bayesian decoders given noisy or incomplete spike trains
Smith, C., & Paninski, L. (2013) · Network Computation in Neural Systems · doi:10.3109/0954898x.2013.789568 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Lam Siu Kwan, Pitrou Antoine, and Seibert Stanley (2015). “ Numba: A LLVM-based Python JIT Compiler”. Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC. LLVM ‘15 Austin, Texas: ACM, 7: 1– 7:6.
- Perry Patrick O. (2009). “ Cross-Validation for Unsupervised Learning”. PhD thesis Stanford University.
Cited by
Sorted by citations
High-performance brain-to-text communication via handwriting
Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M., & Shenoy, K. V. (2021) · Nature · doi:10.1038/s41586-021-03506-2 · Cited by 969 · see citing works
Self-citationA neural mechanism for terminating decisions
Stine, G. M., Trautmann, E. M., Jeurissen, D., & Shadlen, M. N. (2023) · Neuron · doi:10.1016/j.neuron.2023.05.028 · Cited by 83 · see citing works
Self-citation
- Authors
- Williams, A. H., Poole, B., Maheswaranathan, N., Dhawale, A. K., Fisher, T., Wilson, C. D., Brann, D. H., Trautmann, E. M., Ryu, S., Shusterman, R., Rinberg, D., Ölveczky, B. P., Shenoy, K. V., & Ganguli, S.
- Affiliations
- Stanford University (US)
- Google (United States) (US)
- Harvard University (US)
- New York University (US)
- Howard Hughes Medical Institute (US)
- Palo Alto Medical Foundation (US)
- University of Oregon (US)
- Corresponding author
- Williams, Alex H.
- Ganguli, Surya
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 105
- Issue
- 2
- Pages
- 246-259.e8
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC7336835
- OpenAlex
- W2991597663
- Cited by
- 102
- References
- 75
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 17 · 2023: 13 · 2022: 11 · 2021: 15 · 2020: 12 · 2019: 3
- FWCI
- 5.96
- Type
- journal article
- Language
- en
- Update notices
- erratum (2023-05-17)
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Burroughs Wellcome Fund
- Simons Foundation (325380, 543045)
- Howard Hughes Medical Institute
- National Science Foundation (IGERT 0734683)
- Office of Naval Research (N000141812158)
- National Institute of Neurological Disorders and Stroke (R01-DC013797, R01-NS099323-01, R01-DC014366)
- Alfred P. Sloan Foundation
- Charles A. King Trust
- McKnight Foundation
- Life Sciences Research Foundation
- National Institutes of Health (1F31NS089376-01, 8DP1HD075623)
- U.S. Department of Energy
- James S. McDonnell Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Undergraduate Neuroscience Education and Research
- Keywords
- time warping, local field potentials, olfaction, motor cortex, non-human primates, rats, mice
- MeSH terms
- Action Potentials, Animals, Macaca mulatta, Male, Mice, Transgenic, Microinjections, Motor Cortex, Neurons, Pattern Recognition, Automated, Peptide Fragments, Proteins, Time Factors, Amyloid beta-Protein Precursor, Mice, Rats, Gene Knock-In Techniques, Primary Cell Culture
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
