| Autor: |
Nasir eAhmad, Irina eHiggins, Kerry Marie May Walker, Simon Maitland Stringer |
| Jazyk: |
angličtina |
| Rok vydání: |
2016 |
| Předmět: |
|
| Zdroj: |
Frontiers in Computational Neuroscience, Vol 10 (2016) |
| Druh dokumentu: |
article |
| ISSN: |
1662-5188 |
| DOI: |
10.3389/fncom.2016.00024 |
| Popis: |
Attempting to explain the perceptual qualities of pitch has proven to be, and remains, a difficult problem. The wide range of sounds which illicit 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 simplified system in which pitch representing neurons are easily produced under a highly biological 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 missing fundamental sounds. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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