Basilar-membrane responses to broadband noise modeled using linear filters with rational transfer functions

Alberto Recio-Spinoso*, Yun Hui Fan, Mario A. Ruggero

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Basilar-membrane responses to white Gaussian noise were recorded using laser velocimetry at basal sites of the chinchilla cochlea with characteristic frequencies near 10 kHz and first-order Wiener kernels were computed by cross correlation of the stimuli and the responses. The presence or absence of minimum-phase behavior was explored by fitting the kernels with discrete linear filters with rational transfer functions. Excellent fits to the kernels were obtained with filters with transfer functions including zeroes located outside the unit circle, implying nonminimum-phase behavior. These filters accurately predicted basilar-membrane responses to other noise stimuli presented at the same level as the stimulus for the kernel computation. Fits with all-pole and other minimum-phase discrete filters were inferior to fits with nonminimum-phase filters. Minimum-phase functions predicted from the amplitude functions of the Wiener kernels by Hilbert transforms were different from the measured phase curves. These results, which suggest that basilar-membrane responses do not have the minimum-phase property, challenge the validity of models of cochlear processing, which incorporate minimum-phase behavior.

Original languageEnglish (US)
Article number5483155
Pages (from-to)1456-1465
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Issue number5
StatePublished - May 2011


  • Autoregressive moving-average (ARMA) modeling
  • Hilbert transform
  • Wiener kernels
  • basilar membrane (BM)
  • cochlea
  • minimum phase

ASJC Scopus subject areas

  • Biomedical Engineering


Dive into the research topics of 'Basilar-membrane responses to broadband noise modeled using linear filters with rational transfer functions'. Together they form a unique fingerprint.

Cite this