Characterizing auditory neurons using the Wigner and Rihacek distributions: a comparison

Area of Science:

• Neuroscience
• Signal Processing
• Bioacoustics

Background:

• Sound characterization is crucial for understanding auditory processing.
• Frequency-time (FT) domain analysis, using tools like Wigner distribution function (WDF) and Rihacek energy density function (RDF), is effective for dynamic signals.
• Auditory neurons exhibit complex responses to sound, necessitating advanced characterization methods.

Purpose of the Study:

• To introduce novel applications of WDF and RDF for characterizing auditory neurons.
• To develop a new method for quantifying auditory neuron phase locking to wideband noise.
• To compare the efficacy of WDF and RDF in representing auditory neuron tuning and signal characteristics.

Main Methods:

• Utilized the Wigner distribution function (WDF) to analyze auditory neuron tuning under wideband noise.
• Developed and applied a novel method to quantify auditory neuron phase locking using FT functions.
• Compared WDF and Rihacek energy density function (RDF) performance across varying signal bandwidths and for characterizing auditory neurons in anurans.

Main Results:

• WDF and RDF showed similar performance for narrow-band signals but diverged with increasing bandwidth.
• WDF demonstrated a smaller bandwidth than RDF when characterizing auditory neurons.
• A new method successfully quantified auditory neuron phase lock to wideband noise by analyzing spectral properties.

Conclusions:

• The Wigner distribution function (WDF) provides a more refined characterization of auditory neuron responses, especially for complex sounds and phase locking.
• The developed phase-locking quantification method offers valuable insights into neural coding of auditory information.
• WDF is a powerful tool for analyzing auditory neuron dynamics, offering advantages over RDF for broader bandwidth signals and neural activity.