Harmonic training and the formation of pitch representation in a neural network model of the auditory brain

Journal article

Attempting to explain the perceptual qualities of pitch has proven to be, and remains, a difficult problem. The wide range of sounds which elicit pitch and a lack of agreement across neurophysiological studies on how pitch is encoded by the brain have made this attempt more difficult. In describing the potential neural mechanisms by which pitch may be processed, a number of neural networks have been proposed and implemented. However, no unsupervised neural networks with biologically accurate cochlear inputs have yet been demonstrated. This paper proposes a simple system in which pitch representing neurons are produced in a biologically plausible setting. Purely unsupervised regimes of neural network learning are implemented and these prove to be sufficient in identifying the pitch of sounds with a variety of spectral profiles, including sounds with missing fundamental frequencies and iterated rippled noises.

Authors: Ahmad, N; Higgins, I; Walker, K; Stringer, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Frontiers Media S.A.
• Journal: Frontiers in computational neuroscience
• Volume: 10
• Issue: MAR
• Pages: 24
• Publication date: 2016-03-23
• Acceptance date: 2016-03-07
• DOI: 10.3389/fncom.2016.00024
• EISSN: 1662-5188
• ISSN: 1662-5188
• Language: English
• Keywords: competitive neural network; SBTMR; auditory brain; unsupervised learning; pitch identification; harmonic training