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Symposium and Mini-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 3 February 2021, 41 (5) 866-872; 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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    Figure 1.

    A dynamic systems view on brain and behavior in the context of perception, action, and cognition. This perspective challenges the theoretical framework of a centralized clocking mechanism by showing how temporal processing in perception and sensorimotor actions is achieved by coordinating perceptual, motor, and cognitive processes.

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    Figure 2.

    Simplified virtual throwing task. A, In the virtual task, the participant performs forearm movements via a manipulandum and throws a virtual ball to hit a target on the screen. The error is defined and calculated as the shortest distance that the ball trajectory achieves to the target. The time at which this closest distance occurs can differ between trials. B, The task has redundancy as infinitely many different ball releases (angle and velocity at ball release) can achieve a given error. The combinations of angle and velocity that achieve zero error define the solution manifold (green band). Orange line indicates an exemplary arm trajectory plotted in the same space. It closely aligns with the solution manifold where a “timing window” can be defined. Any ball release within this window achieves a zero-error target hit (Zhang et al., 2018). C, Continuous arm movements plotted in phase space spanned by position and velocity, display a closed orbit, indicating periodicity. With practice, successive throws develop a stable periodic pattern (from day 1 to day 4). Black dots indicate the ball releases. The variability of these trajectories significantly decreases from day 1 to day 4. Red line indicates a Poincare section, where the intersections of the arm trajectory are analyzed to test for stability (Zhang and Sternad, 2019). D, Velocity profile of two successive throwing movements illustrates different temporal intervals defined by kinematic landmarks. Red dot indicates the ball release time. The interval between the start of the movement to the ball release (∼300 ms) is most critical and positively affects interval perception.

  • Figure 3.
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    Figure 3.

    Representation of time during time interval reproduction and rhythmic timing. A, Top, Time interval production task. Monkeys were required to estimate a sample interval demarcated by Ready and Set, and reproduce that interval by a delayed motor response (Go). Sample intervals were drawn from one of two prior distributions: Short or Long. Bottom, A schematic showing the curved neural trajectory during the Ready-Set epoch for the Short prior condition. Linear readout of time intervals from the curved neural trajectory (left) generates biased internal estimates of the sample interval (middle) and reduces variability near the extrema of the prior distribution (right). B, Top, Synchronization task. Monkeys were required to tap (circles) synchronously three intervals (SO1-SO3) to an external metronome (arrows). The interstimulus interval was either Short or Long. Bottom, Neural trajectories during the synchronization task. The trajectory starts from a tapping manifold (black line), completes a cycle during every intertap interval, and returns to the tapping manifold. The tapping manifold is invariant across durations and serial order elements of the task. The metronome's tempo modulates the amplitude of the trajectories and the serial order element as the third axes in the state population.

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The Journal of Neuroscience: 41 (5)
Journal of Neuroscience
Vol. 41, Issue 5
3 Feb 2021
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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 3 February 2021, 41 (5) 866-872; 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 3 February 2021, 41 (5) 866-872; DOI: 10.1523/JNEUROSCI.1652-20.2020
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Keywords

  • beat based timing
  • dynamic systems
  • interval based timing
  • motor timing
  • sensorimotor control
  • temporal processing

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