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Methods and Analyses to Uncover the Muscle Pattern-Generating Mechanisms of the Spinal Cord and Motor Cortex Using Deep Learning-Based Dynamical Systems Models
Methods and Analyses to Uncover the Muscle Pattern-Generating Mechanisms of the Spinal Cord and Motor Cortex Using Deep Learning-Based Dynamical Systems Models
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102909
- ISBN
- 9798265406453
- DDC
- 000
- 서명/저자
- Methods and Analyses to Uncover the Muscle Pattern-Generating Mechanisms of the Spinal Cord and Motor Cortex Using Deep Learning-Based Dynamical Systems Models
- 발행사항
- [Sl] : Georgia Institute of Technology, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 167 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Pandarinath, Chethan;Yong, Nicholas Au.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2023.
- 초록/해제
- 요약The motor nervous system can flexibly generate a wide range of voluntary movements by coordinating the time-varying activity of muscles with precision on the order of milliseconds. Understanding the natural muscle pattern-generating functions of the motor cortex and spinal cord is critical to the development of technologies like brain-machine interfaces that aim to restore motor function through the stimulation of muscles.The objectives of this thesis were to perform high-fidelity analyses of the neural population activity in the spinal cord and motor cortex to understand their roles in generating patterns of muscle activations. The first aim of this research was to develop methods to generate high-fidelity muscle activation estimates from EMG recordings. In chapter 2, we adapted a large-scale deep learning-based dynamical systems model optimization framework for cortical spiking activity (AutoLFADS) and demonstrated its broad application to de-noise multi-muscle EMG recordings. The second aim of this research was to investigate the activity of spinal interneuron populations to understand their role in locomotor pattern generation. In chapter 3, we pioneered application of AutoLFADS to spinal interneuron and muscle recordings to uncover precise relationships. The third aim of this research was to study the relationship between motor cortical populations and muscle activations. In chapter 4, we studied the extent to which linear readouts could predict muscle activations from M1 during a complex reach-to-grasp task. Finally, in chapter 5, we developed a platform to perform manifold alignment of M1 population activity that led to stable prediction of muscle activations over 95 days.
- 일반주제명
- Ankle
- 일반주제명
- Kinematics
- 일반주제명
- Behavior
- 일반주제명
- Deep learning
- 일반주제명
- Brain research
- 일반주제명
- Neural networks
- 일반주제명
- Nervous system
- 일반주제명
- Electromyography
- 일반주제명
- Muscle function
- 일반주제명
- Dynamical systems
- 일반주제명
- Visualization
- 일반주제명
- Spinal cord injuries
- 일반주제명
- Mathematics
- 일반주제명
- Medicine
- 일반주제명
- Neurosciences
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798265406453
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■035 ▼a(MiAaPQ)GeorgiaTech76788
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a000
■1001 ▼aWimalasena, Lahiru N.
■24510▼aMethods and Analyses to Uncover the Muscle Pattern-Generating Mechanisms of the Spinal Cord and Motor Cortex Using Deep Learning-Based Dynamical Systems Models
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a167 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Pandarinath, Chethan;Yong, Nicholas Au.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2023.
■520 ▼aThe motor nervous system can flexibly generate a wide range of voluntary movements by coordinating the time-varying activity of muscles with precision on the order of milliseconds. Understanding the natural muscle pattern-generating functions of the motor cortex and spinal cord is critical to the development of technologies like brain-machine interfaces that aim to restore motor function through the stimulation of muscles.The objectives of this thesis were to perform high-fidelity analyses of the neural population activity in the spinal cord and motor cortex to understand their roles in generating patterns of muscle activations. The first aim of this research was to develop methods to generate high-fidelity muscle activation estimates from EMG recordings. In chapter 2, we adapted a large-scale deep learning-based dynamical systems model optimization framework for cortical spiking activity (AutoLFADS) and demonstrated its broad application to de-noise multi-muscle EMG recordings. The second aim of this research was to investigate the activity of spinal interneuron populations to understand their role in locomotor pattern generation. In chapter 3, we pioneered application of AutoLFADS to spinal interneuron and muscle recordings to uncover precise relationships. The third aim of this research was to study the relationship between motor cortical populations and muscle activations. In chapter 4, we studied the extent to which linear readouts could predict muscle activations from M1 during a complex reach-to-grasp task. Finally, in chapter 5, we developed a platform to perform manifold alignment of M1 population activity that led to stable prediction of muscle activations over 95 days.
■590 ▼aSchool code: 0078.
■650 4▼aAnkle
■650 4▼aKinematics
■650 4▼aBehavior
■650 4▼aDeep learning
■650 4▼aBrain research
■650 4▼aNeural networks
■650 4▼aNervous system
■650 4▼aElectromyography
■650 4▼aMuscle function
■650 4▼aDynamical systems
■650 4▼aVisualization
■650 4▼aSpinal cord injuries
■650 4▼aMathematics
■650 4▼aMedicine
■650 4▼aNeurosciences
■690 ▼a0800
■690 ▼a0405
■690 ▼a0564
■690 ▼a0317
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365990▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
