Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks

Nafise Erfanian Saeedi, Peter J. Blamey, Anthony N. Burkitt, David B. Grayden

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)


Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

Original languageEnglish
Article numbere1004860
Number of pages21
JournalPLoS Computational Biology
Issue number4
Publication statusPublished - 6 Apr 2016
Externally publishedYes


  • neurons
  • pitch perception
  • action potentials
  • vowels
  • auditory system
  • auditory nerves
  • membrane potential
  • learning

Cite this