A spiking neuron representation of auditory signals

Guoping Wang, Misha Pavel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


We describe a model of the auditory system in which a population of spiking neurons with limited sampling rates represents the magnitude and phase of high bandwidth auditory signals. The basic premise of this model is based on the fact that each peripheral auditory neuron appears to have a very narrow band tuning characteristics. The signal in each narrow-band channel can, therefore, be sampled at frequencies that are much lower than the center frequency of the band, e.g., ≤ 50 Hz and consistent with the capabilities of neurons. The new idea here is that the system can use non-uniform sampling, consistent with the refractory periods of the neurons, to capture both the amplitude of the modulation and the phase of the carrier signal. The computational model described in this paper consists of a short-term FFT analysis combined with overlap-add and a sampling process where magnitude is digitized but phase is represented using a temporal code of spiking neurons. The coding/decoding mechanism is using knowledge of the properties of the refractory period. We show that this model can represent arbitrary signals, but redundant signals such as speech are represented with higher accuracy than uncorrelated noise. We note that this basic coding approach may be useful for representation of signals in situation where binary representation is not feasible.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Number of pages6
StatePublished - 2005
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Publication series

NameProceedings of the International Joint Conference on Neural Networks


OtherInternational Joint Conference on Neural Networks, IJCNN 2005
CityMontreal, QC

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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