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Neural Population Dynamics Underlyin...

Vyas, Saurabh.

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Neural Population Dynamics Underlying Motor Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neural Population Dynamics Underlying Motor Learning./
Author:	Vyas, Saurabh.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	122 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:	Dissertations Abstracts International82-02B.
Subject:	Neurosciences. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28121552
ISBN:	9798662514348

Neural Population Dynamics Underlying Motor Learning.
Vyas, Saurabh.

Neural Population Dynamics Underlying Motor Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 122 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--Stanford University, 2020.

This item must not be sold to any third party vendors.

Several organisms often demonstrate the ability to produce highly adaptable and increasingly sophisticated movements. The computations required to produce even simple arm movements, e.g., reaching for a coffee cup, involve generating complex time varying patterns of neural activity. Learning poses an even greater challenge: the brain must somehow select a set of neural commands, from billions of possible activity patterns, that best help the organism achieve its movement objectives. Consider a common scenario where a subject learns a motor task in one context, and now wishes to perform the same task in a very different context. Certainly, in some cases this is possible. What are those cases? What is the neural mechanism that facilitates this transfer of learning? We developed a "covert learning" paradigm whereby Rhesus monkeys can perform the same visuomotor learning task either overtly using arm movements, or covertly using a brain-machine interface. In the covert context, no overt movements can be made, and thus monkeys learn to generate patterns of neural activity that drive the brain-machine interface to perform the task. Using this paradigm, we demonstrated that learning can indeed transfer across contexts in order to improve overt behavior. We studied the neural activity in premotor and primary motor cortex during transfer learning at a population level. Intriguingly, we discovered that a key ingredient driving transfer is a shared neural substrate consisting of neural activity during motor preparation (this is known as preparatory activity or the preparatory state). Even on the single-trial level, behavioral improvements due to visuomotor learning are accompanied with systematic changes to the motor cortical preparatory state. Standard theories of visuomotor learning suggest that a trial-by-trial learning process performs computations based on an efference copy of the outgoing motor command, and sensory feedback during motor execution. These computations result in an update, which improves the behavior on subsequent trials. Our results suggest that this update occurs (at minimum) during motor preparation. Finally, through microstimulation experiments, we established the first causal link between motor preparation and visuomotor learning. Concretely, we found that neural activity during motor preparation causally interacts with the update computations of a trial-by-trial learning process. Disrupting preparatory activity does not affect the current trial, but instead influences the update computation in a fashion that manifests as disruption to learning on subsequent trials. More generally, these experiments reveal that the learning process (a) has access to the preparatory state, (b) the ability to assess how good the current preparatory state is, and (c) the ability to influence the preparatory state on both current and future trials. Taken together, this thesis reveals that neural activity before the onset of movement, or even in the absence of movement altogether, could play a fundamental role in the algorithm underlying the neural control of movement.

ISBN: 9798662514348Subjects--Topical Terms:

588700
Neurosciences.
Subjects--Index Terms:

Visuomotor learning

Neural Population Dynamics Underlying Motor Learning.
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520 $a Several organisms often demonstrate the ability to produce highly adaptable and increasingly sophisticated movements. The computations required to produce even simple arm movements, e.g., reaching for a coffee cup, involve generating complex time varying patterns of neural activity. Learning poses an even greater challenge: the brain must somehow select a set of neural commands, from billions of possible activity patterns, that best help the organism achieve its movement objectives. Consider a common scenario where a subject learns a motor task in one context, and now wishes to perform the same task in a very different context. Certainly, in some cases this is possible. What are those cases? What is the neural mechanism that facilitates this transfer of learning? We developed a "covert learning" paradigm whereby Rhesus monkeys can perform the same visuomotor learning task either overtly using arm movements, or covertly using a brain-machine interface. In the covert context, no overt movements can be made, and thus monkeys learn to generate patterns of neural activity that drive the brain-machine interface to perform the task. Using this paradigm, we demonstrated that learning can indeed transfer across contexts in order to improve overt behavior. We studied the neural activity in premotor and primary motor cortex during transfer learning at a population level. Intriguingly, we discovered that a key ingredient driving transfer is a shared neural substrate consisting of neural activity during motor preparation (this is known as preparatory activity or the preparatory state). Even on the single-trial level, behavioral improvements due to visuomotor learning are accompanied with systematic changes to the motor cortical preparatory state. Standard theories of visuomotor learning suggest that a trial-by-trial learning process performs computations based on an efference copy of the outgoing motor command, and sensory feedback during motor execution. These computations result in an update, which improves the behavior on subsequent trials. Our results suggest that this update occurs (at minimum) during motor preparation. Finally, through microstimulation experiments, we established the first causal link between motor preparation and visuomotor learning. Concretely, we found that neural activity during motor preparation causally interacts with the update computations of a trial-by-trial learning process. Disrupting preparatory activity does not affect the current trial, but instead influences the update computation in a fashion that manifests as disruption to learning on subsequent trials. More generally, these experiments reveal that the learning process (a) has access to the preparatory state, (b) the ability to assess how good the current preparatory state is, and (c) the ability to influence the preparatory state on both current and future trials. Taken together, this thesis reveals that neural activity before the onset of movement, or even in the absence of movement altogether, could play a fundamental role in the algorithm underlying the neural control of movement.
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