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Tracking fast unpredictable movements is a valuable skill, applicable in many situations. In the animal kingdom, the context includes the action of a predator chasing its prey that is running and dodging at high speeds, like a cheetah chasing a gazelle. The sensorimotor control system (SCS) is responsible for such actions and its performance clearly depends on the computing power of neurons, delays between brain and muscles, and the dynamics of muscles involved. Despite these obvious factors that set the limits on how fast an animal can track a moving object, tracking performance of the SCS and its dependence on neural computing, delays, and muscle dynamics have not been explicitly quantified. In this program, we will build upon new theory developed using feedback control principles and an appropriately simplified model of the SCS to identify how neural computing, delays, and muscles interact during the generation of fast movements. Therefore if one component is compromised, we can take advantage of the other components to restore motor performance with assistive neuroprosthetic devices. The program objectives are to first parameterize the major factors (brain and body) limiting fast movements and to derive how these parameters must interact to achieve tracking of fast movements in the SCS. Then, the parameterization and quantified interactions will be tested experimentally in subjects through manipulation of (i) neural computing power, (ii) transmission delays, and (iii) muscle dynamics. If discrepancies emerge between experiments and theory, the SCS model and theory will be modified to explain observation data. Finally, the theoretical model of interactions required to achieve tracking of fast movements will be exploited to apply compensation to account for degradation of some parameters by "boosting" others. More specifically, we will design assistive neuroprosthetic devices for subjects having compromised neural real estate to restore performance of fast movements. For example, if primary motor cortex is compromised due to disease or damage, we can manipulate muscle dynamics by adding the necessary compensatory forces to restore motor performance, and more importantly restore fast and agile movements. Just how one should compensate will be informed by our SCS model and theory.
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EEG Biomarkers Derived from Dynamical Network Models Enable Rapid Paths to Accurate Diagnosis and Effective Treatment of Epilepsy
  • 批准号:
    10665213
  • 项目类别:
  • 资助金额:
    $48.01万
  • 财政年份:
    2023
  • 负责人:
    Sridevi V. Sarma
  • 依托单位:
Administrative Core
  • 批准号:
    10707072
  • 项目类别:
  • 资助金额:
    $83.05万
  • 财政年份:
    2022
  • 负责人:
    Sridevi V. Sarma
  • 依托单位:
Using Feedback Control to Suppress Seizure Genesis in Epilepsy
  • 批准号:
    9920327
  • 项目类别:
  • 资助金额:
    $0.83万
  • 财政年份:
    2019
  • 负责人:
    Sridevi V. Sarma
  • 依托单位:
CRCNS: MOVE!-MOdeling of fast Movement for Enhancement via neuroprosthetics
  • 批准号:
    10611557
  • 项目类别:
  • 资助金额:
    $1.35万
  • 财政年份:
    2018
  • 负责人:
    Sridevi V. Sarma
  • 依托单位:
国内基金
海外基金
Agonist-GPR119-Gs复合物的结构生物学研究
  • 批准号:
    32000851
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2020
  • 负责人:
    乔安娜
  • 依托单位: