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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
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A robust sound perception model suitable for neuromorphic implementation.

Martin Coath1, Sadique Sheik2, Elisabetta Chicca3

  • 1Cognition Institute, Plymouth University Plymouth, UK ; Faculty of Health and Human Sciences, School of Psychology, Plymouth University Plymouth, UK.

Frontiers in Neuroscience
|January 31, 2014
PubMed
Summary
This summary is machine-generated.

This study shows a neuromorphic system with spiking neurons learns to process auditory information robustly. The system demonstrates stable performance even with noisy or variable speech stimuli, crucial for real-world applications.

Keywords:
VLSIauditoryinformationmodelingneuromorphicplasticity

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Signal Processing

Background:

  • Neuromorphic systems offer a biologically inspired approach to artificial intelligence.
  • Auditory processing in mammals involves complex neural networks with learning capabilities.
  • Dynamic feature sensitivity is crucial for processing naturalistic and variable sensory input.

Purpose of the Study:

  • To demonstrate the robustness of a spiking neural network to variations in auditory stimuli.
  • To analyze the information processing capabilities of the neuromorphic system using information-theoretic measures.
  • To assess the system's potential for real-world auditory applications.

Main Methods:

  • Implemented a hybrid analog/digital neuromorphic system with spiking neurons and STDP learning.
  • Exposed the network to various auditory stimuli, including naturalistic and speech-derived patterns.
  • Analyzed network response variability to noisy stimuli and characterized system acuity.
  • Evaluated robustness against variable presentation rates and real-world sound samples.

Main Results:

  • The neuromorphic network exhibited dynamic feature sensitivity through learning.
  • The system demonstrated significant robustness to variations and noise in stimulus patterns.
  • Information-theoretic analysis provided a quantitative measure of network acuity.
  • The approach proved effective with speech samples and variable presentation rates.

Conclusions:

  • The developed neuromorphic system shows robustness, a key feature for realistic electronic systems.
  • Quantitative analysis using information theory offers a basis for comparing neuromorphic designs.
  • The findings support the potential applicability of this approach to processing real-world sounds.