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Dynamic sensor adaptation based on efferent feedback for adaptive bio-inspired sound source localization.

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  • 1Group of Biomedical Sensor Systems and Microsystems, Universität Ulm, Ulm, Germany.

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Summary
This summary is machine-generated.

Sensor adaptation using efferent feedback can improve artificial auditory systems' sound localization. This bio-inspired neuromorphic system demonstrates how feedback tuning overcomes sensor mismatches and influences neural responses for better performance.

Keywords:
dynamic sensor adaptationefferent feedbackinteraural level differenceslateral superior olivary complexneural networkneuromorphic computingrecurrent processingsound source localization

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Bio-inspired Engineering

Background:

  • Auditory perception and localization are crucial for survival, yet artificial systems lag behind biological capabilities, especially in complex environments.
  • Efferent feedback, a mechanism in biological auditory systems, is explored as a method to enhance artificial auditory processing.
  • Existing artificial auditory systems struggle with dynamic and noisy conditions, limiting their sound localization accuracy.

Purpose of the Study:

  • To investigate if sensor adaptation, driven by efferent feedback, can improve sound localization performance in artificial auditory systems.
  • To develop and analyze a neuromorphic system architecture inspired by human interaural level difference (ILD) processing and efferent feedback.
  • To evaluate the impact of different feedback configurations on the neural processing stage.

Main Methods:

  • A neuromorphic system with two bio-inspired acoustic sensors and a neural processing stage (MNTB and LSO neurons) was designed.
  • Analysis involved measuring LSO neuron responses with and without efferent feedback across varying ILDs.
  • Simulations explored four feedback configurations from LSO neurons to acoustic sensors using synthetically generated sounds.

Main Results:

  • Feedback tuning effectively compensated for fabrication-induced mismatches between MEMS sensors.
  • Different feedback configurations and simulation parameters significantly influenced the LSO neuron's response to varying ILDs.
  • The study quantified the impact of efferent feedback on auditory sensor performance and neural signal processing.

Conclusions:

  • Efferent feedback is a viable mechanism for enhancing the performance and robustness of artificial auditory systems.
  • The proposed neuromorphic architecture demonstrates the potential of bio-inspired sensor adaptation for improved sound localization.
  • Further research into feedback mechanisms can lead to more sophisticated artificial auditory perception systems.