Continuous input drives motor cortical dynamics during reaching

Area of Science:

• Neuroscience
• Computational Neuroscience
• Motor Control

Background:

• Current models propose autonomous internal processing drives motor cortical activity.
• The role of continuous external input in shaping neural dynamics during behavior remains less understood.

Purpose of the Study:

• To investigate the influence of continuous external input versus intrinsic processing on motor cortical neuron activity during reaching.
• To determine the primary drivers of state transitions observed in neural populations during motor tasks.

Main Methods:

• Utilized hybrid neural networks (HNNs) combining artificial connectivity with empirical firing rates.
• Recorded neural population activity during a reaching task.
• Employed HNNs to model neural systems and test hypotheses about input sources.

Main Results:

• Observed distinct state transitions in motor cortical neuron firing rates during reaching.
• HNNs accurately reproduced recorded firing rates and population state transitions.
• Demonstrated that extrinsic, episodically modulated input, rather than intrinsic processing, was responsible for the observed neural dynamics.
• Identified that episodic external drive alters pre-threshold input integration statistics, causing state transitions.

Conclusions:

• Continuous external input is a key driver of dynamic activity and state transitions in the motor cortex during reaching.
• Findings challenge the primacy of intrinsic action in current models of cortical function.
• Hybrid neural networks provide a powerful tool for dissecting the contributions of intrinsic and extrinsic factors in neural systems.