Neural computations underlying the exertion of force: a model

Area of Science:

• Neuroscience
• Computational Biology
• Motor Control

Background:

• Supraspinal signals are crucial for voluntary movement and force exertion.
• The precise mechanisms of signal convergence in the spinal cord remain incompletely understood.
• Existing models often lack comprehensive validation against diverse experimental data.

Purpose of the Study:

• To develop and validate a computational model simulating supraspinal signal integration in the spinal cord for force generation.
• To investigate the neural network architecture underlying the transformation of neuronal signals into motor output.
• To predict the relationship between neural connectivity and motor control performance.

Main Methods:

• Development of a three-layered neural network model representing supraspinal populations, spinal interneurons, and motoneuronal pools.
• Integration of the neural network with a biomechanical model of a two-joint, six-muscle arm.
• Training the network using an approach validated against experimental data from human, frog, and monkey studies.

Main Results:

• The model successfully simulated force generation consistent with human arm stiffness, frog spinal cord stimulation, and monkey motor cortical activity.
• A specific pattern of connectivity was predicted: connection weights correlate with the directional preference of neuronal units.
• Simulations showed that summed neural signals closely approximated the vector sum of individual forces, despite complex nonlinearities.

Conclusions:

• The developed model provides a plausible mechanism for supraspinal force coding signal convergence in the spinal cord.
• The findings highlight the importance of specific connection patterns between neuronal populations for precise motor control.
• The model offers a framework for understanding the neural basis of force generation and its variability across species.