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Symposium

Neural Encoding and Representation of Time for Sensorimotor Control and Learning

Ramesh Balasubramaniam, Saskia Haegens, Mehrdad Jazayeri, Hugo Merchant, Dagmar Sternad and Joo-Hyun Song
Journal of Neuroscience 30 December 2020, JN-SY-1652-20; https://doi.org/10.1523/JNEUROSCI.1652-20.2020
Ramesh Balasubramaniam
1University of California Merced, Merced, CA 95343
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Saskia Haegens
2Columbia University, New York, NY 10027
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Mehrdad Jazayeri
3Massachusetts Institute of Technology, Cambridge, MA 02139
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Hugo Merchant
4Instituto de Neurobiologia, UNAM, campus Juriquilla, Querétaro, México 76230
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Dagmar Sternad
5Northeastern University, Boston, MA 02115
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Joo-Hyun Song
6Brown University, Providence, RI 02912
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Abstract

The ability to perceive and produce movements in the real world with precise timing is critical for survival in animals, including humans. However, research on sensorimotor timing has rarely considered the tight interrelation between perception, action, and cognition. In this review, we present new evidence from behavioral, computational, and neural studies in humans and nonhuman primates, suggesting a pivotal link between sensorimotor control and temporal processing, as well as describing new theoretical frameworks regarding timing in perception and action. We first discuss the link between movement coordination and interval-based timing by addressing how motor training develops accurate spatiotemporal patterns in behavior and influences the perception of temporal intervals. We then discuss how motor expertise results from establishing task-relevant neural manifolds in sensorimotor cortical areas and how the geometry and dynamics of these manifolds help reduce timing variability. We also highlight how neural dynamics in sensorimotor areas are involved in beat-based timing. These lines of research aim to extend our understanding of how timing arises from and contributes to perceptual-motor behaviors in complex environments to seamlessly interact with other cognitive processes.

Footnotes

  • The authors declare no competing financial interests.

  • This work was supported by National Science Foundation BCS-1460633 and DGE-1633722 to R.B.; National Institutes of Health P50-MH109429 to S.H.; National Institutes of Health R01-NS078127, Simons Foundation SCGB Collaborative Grant, McKnight Scholar Award, and Klingenstein-Simons Fellowship Award to M.J.; Consejo Nacional de Ciencia y Tecnología A1-S-8430 to H.M.; National Institutes of Health R01-HD087089, National Science Foundation M3X-1825942, CRCNS-1723998, and NRI-1637854 to D.S.; and National Science Foundation BCS-1555006 and BCS-1849169 to J.-H.S. We thank Drs. M. Sadeghi and Z. Zhang for assisting with Figures 1 and 2.

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Neural Encoding and Representation of Time for Sensorimotor Control and Learning
Ramesh Balasubramaniam, Saskia Haegens, Mehrdad Jazayeri, Hugo Merchant, Dagmar Sternad, Joo-Hyun Song
Journal of Neuroscience 30 December 2020, JN-SY-1652-20; DOI: 10.1523/JNEUROSCI.1652-20.2020

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Neural Encoding and Representation of Time for Sensorimotor Control and Learning
Ramesh Balasubramaniam, Saskia Haegens, Mehrdad Jazayeri, Hugo Merchant, Dagmar Sternad, Joo-Hyun Song
Journal of Neuroscience 30 December 2020, JN-SY-1652-20; DOI: 10.1523/JNEUROSCI.1652-20.2020
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