Emergence of reproducible spatiotemporal activity during motor learning
Abstract
The motor cortex is capable of reliably driving complex movements yet exhibits considerable plasticity during motor learning. These observations suggest that the fundamental relationship between motor cortex activity and movement may not be fixed but is instead shaped by learning; however, to what extent and how motor learning shapes this relationship are not fully understood. Here we addressed this issue by using in vivo two-photon calcium imaging to monitor the activity of the same population of hundreds of layer 2/3 neurons while mice learned a forelimb lever-press task over two weeks. Excitatory and inhibitory neurons were identified by transgenic labelling. Inhibitory neuron activity was relatively stable and balanced local excitatory neuron activity on a movement-by-movement basis, whereas excitatory neuron activity showed higher dynamism during the initial phase of learning. The dynamics of excitatory neurons during the initial phase involved the expansion of the movement-related population which explored various activity patterns even during similar movements. This was followed by a refinement into a smaller population exhibiting reproducible spatiotemporal sequences of activity. This pattern of activity associated with the learned movement was unique to expert animals and not observed during similar movements made during the naive phase, and the relationship between neuronal activity and individual movements became more consistent with learning. These changes in popu…
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The activity of M1 cortical neurons dynamically changes during motor learning.
“We focused on the primary motor cortex (M1), as the proliferation and differentiation of M1 OPCs is accelerated, and the activity of M1 cortical neurons dynamically changes during motor learning (Huber et al., 2012 ; Masamizu et al., 2014 ; McKenzie et al., 2014 ; Peters, Chen, & Komiyama, 2014 ; Xiao et al., 2016 ).”
From Motor learning requires myelination to reduce asynchrony and spontaneity in neural activity (2020) · cites this paper as Peters, Chen, & Komiyama, 2014
Cites
The columnar and laminar organization of inhibitory connections to neocortical excitatory cells
Kätzel, D., Zemelman, B. V., Buetfering, C., Wölfel, M., & Miesenböck, G. (2011) · Nature Neuroscience · doi:10.1038/nn.2687 · Cited by 241 · see citing works
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Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159 · 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
Thy1-GCaMP6 transgenic mice for neuronal population imaging in vivo
Dana, H., Chen, T.-W., Hu, A., Shields, B. C., Guo, C., Looger, L. L., Kim, D. S., & Svoboda, K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108697 · Cited by 574 · see citing works
Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex
Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N., & Harvey, C. D. (2017) · Cell · doi:10.1016/j.cell.2017.07.021 · Cited by 445 · see citing works
Touch and tactile neuropathic pain sensitivity are set by corticospinal projections
Liu, Y., Latremoliere, A., Li, X., Zhang, Z., Chen, M., Wang, X., Fang, C., Zhu, J., Alexandre, C., Gao, Z., et al. (2018) · Nature · doi:10.1038/s41586-018-0515-2 · Cited by 276 · see citing works
Challenges in measuring and understanding biological noise
Eling, N., Morgan, M. D., & Marioni, J. C. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0130-6 · Cited by 247 · see citing works
Functional and structural underpinnings of neuronal assembly formation in learning
Holtmaat, A., & Caroni, P. (2016) · Nature Neuroscience · doi:10.1038/nn.4418 · Cited by 239 · see citing works
Pott, S. (2017) · eLife · doi:10.7554/elife.23203 · Cited by 230 · see citing works
Circuit Mechanisms of Sensorimotor Learning
Makino, H., Hwang, E. J., Hedrick, N. G., & Komiyama, T. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.029 · Cited by 228 · see citing works
Self-citationAutomated 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
Calcium imaging of sleep-wake related neuronal activity in the dorsal pons
Cox, J., Pinto, L., & Dan, Y. (2016) · Nature Communications · doi:10.1038/ncomms10763 · Cited by 133 · see citing works
Neural reactivations during sleep determine network credit assignment
Gulati, T., Guo, L., Ramanathan, D. S., Bodepudi, A., & Ganguly, K. (2017) · Nature Neuroscience · doi:10.1038/nn.4601 · Cited by 114 · see citing works
In vivo modeling of human neuron dynamics and Down syndrome
Real, R., Peter, M., Trabalza, A., Khan, S., Smith, M. A., Dopp, J., Barnes, S. J., Momoh, A., Strano, A., Volpi, E., et al. (2018) · Science · doi:10.1126/science.aau1810 · Cited by 120 · see citing works
Motor learning requires myelination to reduce asynchrony and spontaneity in neural activity
Kato, D., Wake, H., Lee, P. R., Tachibana, Y., Ono, R., Sugio, S., Tsuji, Y., Tanaka, Y. H., Tanaka, Y. R., Masamizu, Y., et al. (2020) · Glia · doi:10.1002/glia.23713 · Cited by 114 · see citing works
A human-specific modifier of cortical connectivity and circuit function
Schmidt, E. R. E., Zhao, H. T., Park, J. M., Dipoppa, M., Monsalve-Mercado, M. M., Dahan, J. B., Rodgers, C. C., Lejeune, A., Hillman, E. M. C., Miller, K. D., et al. (2021) · Nature · doi:10.1038/s41586-021-04039-4 · Cited by 82 · see citing works
Decision-making behaviors: weighing ethology, complexity, and sensorimotor compatibility
Juavinett, A. L., Erlich, J. C., & Churchland, A. K. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.11.001 · Cited by 82 · see citing works
- Authors
- Peters, A. J., Chen, S. X., & Komiyama, T.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Komiyama, Takaki
- Venue
- Nature
- Published
- 2014-05-04
- Volume
- 510
- Issue
- 7504
- Pages
- 263-267
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2047712100
- Cited by
- 491
- References
- 1
- Citations by year
- 2026: 30 · 2025: 42 · 2024: 54 · 2023: 45 · 2022: 40 · 2021: 41 · 2020: 43 · 2019: 41 · 2018: 40 · 2017: 45 · 2016: 23 · 2015: 38 · 2014: 8
- FWCI
- 21.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Institute on Aging (AG000216)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- motor learning, motor cortex, excitatory neurons, inhibitory neurons, synaptic plasticity, layer 2/3 neurons, mice, two-photon calcium imaging, neural population dynamics
- MeSH terms
- Animals, Calcium, Female, Forelimb, Learning, Male, Models, Neurological, Motor Cortex, Motor Skills, Neural Inhibition, Neuronal Plasticity, Reproducibility of Results, Dendritic Spines, Mice, Spatio-Temporal Analysis
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