The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Single-trial neural dynamics are dominated by richly varied movements
Open accessPublished version of a preprint
Abstract
When experts are immersed in a task, do their brains prioritize task-related activity? Most efforts to understand neural activity during well-learned tasks focus on cognitive computations and task-related movements. We wondered whether task-performing animals explore a broader movement landscape and how this impacts neural activity. We characterized movements using video and other sensors and measured neural activity using widefield and two-photon imaging. Cortex-wide activity was dominated by movements, especially uninstructed movements not required for the task. Some uninstructed movements were aligned to trial events. Accounting for them revealed that neurons with similar trial-averaged activity often reflected utterly different combinations of cognitive and movement variables. Other movements occurred idiosyncratically, accounting for trial-by-trial fluctuations that are often considered ‘noise’. This held true throughout task-learning and for extracellular Neuropixels recordings that included subcortical areas. Our observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity. The authors use a linear model to reveal how neural activity patterns are related to cognition or movements. They find that uninstructed movements dominate single-cell and population activity throughout the brain, outpacing task-related activity.
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DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0209-y · Cited by 5,187
An internal model for sensorimotor integration
Wolpert, D. M., Ghahramani, Z., & Jordan, M. I. (1995) · Science · doi:10.1126/science.7569931 · Cited by 3,553
Internal models in the cerebellum
Wolpert, D. M., Miall, R. C., & Kawato, M. (1998) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01221-2 · Cited by 2,469
Forward Models for Physiological Motor Control
Miall, R. C., & Wolpert, D. M. (1996) · Neural Networks · doi:10.1016/s0893-6080(96)00035-4 · Cited by 2,260
Fully integrated silicon probes for high-density recording of neural activity
Jun, J. J., Steinmetz, N. A., Siegle, J. H., Denman, D. J., Bauza, M., Barbarits, B., Lee, A. K., Anastassiou, C. A., Andrei, A., Aydın, Ç., et al. (2017) · Nature · doi:10.1038/nature24636 · Cited by 2,190
Multiple paired forward and inverse models for motor control
Wolpert, D. M., & Kawato, M. (1998) · Neural Networks · doi:10.1016/s0893-6080(98)00066-5 · Cited by 2,151
Roitman, J. D., & Shadlen, M. N. (2002) · The Journal of Neuroscience · doi:10.1523/jneurosci.22-21-09475.2002 · Cited by 1,589
Spontaneous behaviors drive multidimensional, brainwide activity
Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., & Harris, K. D. (2019) · Science · doi:10.1126/science.aav7893 · Cited by 1,510
Modulation of visual responses by behavioral state in mouse visual cortex
Niell, C. M., & Stryker, M. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.033 · Cited by 1,483
Suite2p: beyond 10,000 neurons with standard two-photon microscopy
Pachitariu, M., Stringer, C., Dipoppa, M., Schröder, S., Rossi, L. F., Dalgleish, H., Carandini, M., & Harris, K. D. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/061507 · Cited by 1,247
Research works printed but not linked: 1
- Karabatsos, G. Marginal maximum likelihood estimation methods for the tuning parameters of ridge, power ridge, and generalized ridge regression. Commun. Stat. Simulat. (2017). · doi:10.1080/03610918.2017.1321119
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Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings.
Steinmetz, N. A., Aydin, C., Lebedeva, A., Okun, M., Pachitariu, M., Bauza, M., Beau, M., Bhagat, J., Böhm, C., Broux, M., et al. (2021) · Science · doi:10.1126/science.abf4588 · Cited by 1,173
Questions and controversies in the study of time-varying functional connectivity in resting fMRI
Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., Kucyi, A., Liégeois, R., Lindquist, M. A., McIntosh, A. R., et al. (2020) · Network Neuroscience · doi:10.1162/netn_a_00116 · Cited by 648
Deep learning tools for the measurement of animal behavior in neuroscience
Mathis, M. W., & Mathis, A. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.10.008 · Cited by 450
Neuromodulation of brain state and behavior
McCormick, D. A., Nestvogel, D. B., & He, B. J. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100219-105424 · Cited by 303
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 328
Self-citation
Metadata
- Authors
- Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S., & Churchland, A. K.
- Venue
- Nature Neuroscience
- Published
- 2019-09-24
- Volume
- 22
- Issue
- 10
- Pages
- 1677-1686
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2976024218
- Cited by
- 1,075
- References
- 72
- FWCI
- 56.24
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
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