ArticleFrontiers in computational neuroscience2016
Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain.
Article in Frontiers in computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dissociating the neural codes for multiple pitch perception in humans.bioRxiv : the preprint server for biology · 2025Article
- Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception.Nature communications · 2021Article
- Periodicity Pitch Perception.Frontiers in neuroscience · 2020Article
- Neural encoding of sensory and behavioral complexity in the auditory cortex.Current opinion in neurobiology · 2018Review
- Modeling Pitch Perception With an Active Auditory Model Extended by Octopus Cells.Frontiers in neuroscience · 2018Article
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Authors and funding
4 authors.
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Abstract
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.
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