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Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
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Updated: Jul 13, 2025

Force and Position Control in Humans - The Role of Augmented Feedback
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Distributed feedforward and feedback cortical processing supports human speech production.

Ran Wang1, Xupeng Chen1, Amirhossein Khalilian-Gourtani1

  • 1Electrical and Computer Engineering Department, New York University, New York, NY 11201.

Proceedings of the National Academy of Sciences of the United States of America
|October 11, 2023
PubMed
Summary
This summary is machine-generated.

This study reveals that both frontal and temporal brain networks process speech using feedforward and feedback signals simultaneously. This mixed cortical architecture advances understanding of speech processing and neural prosthetics.

Keywords:
auditory feedbackbrain computer interfaceelectrocorticographyspeech motor controlspeech production

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Speech Processing

Background:

  • Speech production relies on feedforward commands and feedback processing.
  • Distinct frontal and temporal cortical networks are involved, but their interaction dynamics are unclear.

Purpose of the Study:

  • To develop a deep learning model for reconstructing speech from neural signals.
  • To disentangle feedforward and feedback processing in cortical speech networks.
  • To investigate the timing and dynamics of these processes.

Main Methods:

  • A novel deep learning architecture was created to translate neural signals into an interpretable speech representation.
  • Learned decoding networks were employed to differentiate feedforward and feedback neural signals.
  • Receptive field analysis quantified the timing of feedforward and feedback processing.

Main Results:

  • A mixed cortical architecture was identified, where frontal and temporal networks process both feedforward and feedback information concurrently.
  • The timing of feedforward and feedback processing was elucidated through derived receptive fields.
  • The model successfully reconstructed speech from neural recordings.

Conclusions:

  • Cortical speech circuitry exhibits a mixed architecture, challenging previous models.
  • The findings advance neural decoding techniques for speech.
  • This research has significant implications for developing advanced neural prosthetics for communication.