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Optimal feedback control to describe multiple representations of primary motor cortex neurons
Yuki Ueyama1,2
1Department of Systems Design Engineering, Faculty of Science and Technology, Seikei University, 3-3-1 Kichijojikitamachi, Musashino, 180-8633, Tokyo, Japan. yuki.ueyama@st.seikei.ac.jp.
Journal of Computational Neuroscience
|June 3, 2017
Summary
Neural activity in the primary motor cortex (M1) is optimized for motor control. Optimal feedback control theory explains how M1 neurons encode information for precise muscle system regulation, considering signal-dependent noise.
Area of Science:
- Neuroscience
- Motor Control
- Computational Neuroscience
Background:
- Primary motor cortex (M1) neurons are crucial for motor control and feedback.
- The precise encoding mechanisms of M1 neurons for musculoskeletal control remain unclear.
Purpose of the Study:
- To investigate the computational mechanisms of M1 using optimal feedback control (OFC) theory.
- To model isometric torque production in a constrained musculoskeletal system.
Main Methods:
- Developed a musculoskeletal model for isometric torque regulation.
- Computed a feedback controller using optimization under physical constraints.
- Analyzed the impact of signal-dependent neuromotor noise.
Main Results:
- Sensory feedback gain in M1 neurons tunes to motor output (e.g., hand force) under noise.
- The distribution of preferred directions (PDs) in M1 neurons is predictable via feedback gain.
- OFC theory can describe M1 population responses and PD distributions.
Conclusions:
- Neural activity in M1 is optimized for musculoskeletal system control.
- OFC provides a framework to understand M1's role in motor encoding and feedback.
- This study offers insights into how M1 represents motor commands and sensory information.
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