A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex.
The auditory pathway consists of multiple stages, from the cochlear nucleus to the auditory cortex. Neurons acting at different stages have different functions and exhibit different response properties. It is unclear whether these stages share a common encoding mechanism. We trained an unsupervised...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-02-01
|
| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC6386396?pdf=render |
| _version_ | 1818014641391927296 |
|---|---|
| author | Qingtian Zhang Xiaolin Hu Bo Hong Bo Zhang |
| author_facet | Qingtian Zhang Xiaolin Hu Bo Hong Bo Zhang |
| author_sort | Qingtian Zhang |
| collection | DOAJ |
| description | The auditory pathway consists of multiple stages, from the cochlear nucleus to the auditory cortex. Neurons acting at different stages have different functions and exhibit different response properties. It is unclear whether these stages share a common encoding mechanism. We trained an unsupervised deep learning model consisting of alternating sparse coding and max pooling layers on cochleogram-filtered human speech. Evaluation of the response properties revealed that computing units in lower layers exhibited spectro-temporal receptive fields (STRFs) similar to those of inferior colliculus neurons measured in physiological experiments, including properties such as sound onset and termination, checkerboard pattern, and spectral motion. Units in upper layers tended to be tuned to phonetic features such as plosivity and nasality, resembling the results of field recording in human auditory cortex. Variation of the sparseness level of the units in each higher layer revealed a positive correlation between the sparseness level and the strength of phonetic feature encoding. The activities of the units in the top layer, but not other layers, correlated with the dynamics of the first two formants (F1, F2) of all phonemes, indicating the encoding of phoneme dynamics in these units. These results suggest that the principles of sparse coding and max pooling may be universal in the human auditory pathway. |
| first_indexed | 2024-04-14T06:46:48Z |
| format | Article |
| id | doaj.art-9cd3c65bc21b488fbc8bc90c5a301f90 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-14T06:46:48Z |
| publishDate | 2019-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-9cd3c65bc21b488fbc8bc90c5a301f902022-12-22T02:07:08ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-02-01152e100676610.1371/journal.pcbi.1006766A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex.Qingtian ZhangXiaolin HuBo HongBo ZhangThe auditory pathway consists of multiple stages, from the cochlear nucleus to the auditory cortex. Neurons acting at different stages have different functions and exhibit different response properties. It is unclear whether these stages share a common encoding mechanism. We trained an unsupervised deep learning model consisting of alternating sparse coding and max pooling layers on cochleogram-filtered human speech. Evaluation of the response properties revealed that computing units in lower layers exhibited spectro-temporal receptive fields (STRFs) similar to those of inferior colliculus neurons measured in physiological experiments, including properties such as sound onset and termination, checkerboard pattern, and spectral motion. Units in upper layers tended to be tuned to phonetic features such as plosivity and nasality, resembling the results of field recording in human auditory cortex. Variation of the sparseness level of the units in each higher layer revealed a positive correlation between the sparseness level and the strength of phonetic feature encoding. The activities of the units in the top layer, but not other layers, correlated with the dynamics of the first two formants (F1, F2) of all phonemes, indicating the encoding of phoneme dynamics in these units. These results suggest that the principles of sparse coding and max pooling may be universal in the human auditory pathway.http://europepmc.org/articles/PMC6386396?pdf=render |
| spellingShingle | Qingtian Zhang Xiaolin Hu Bo Hong Bo Zhang A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex. PLoS Computational Biology |
| title | A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex. |
| title_full | A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex. |
| title_fullStr | A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex. |
| title_full_unstemmed | A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex. |
| title_short | A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex. |
| title_sort | hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex |
| url | http://europepmc.org/articles/PMC6386396?pdf=render |
| work_keys_str_mv | AT qingtianzhang ahierarchicalsparsecodingmodelpredictsacousticfeatureencodinginbothauditorymidbrainandcortex AT xiaolinhu ahierarchicalsparsecodingmodelpredictsacousticfeatureencodinginbothauditorymidbrainandcortex AT bohong ahierarchicalsparsecodingmodelpredictsacousticfeatureencodinginbothauditorymidbrainandcortex AT bozhang ahierarchicalsparsecodingmodelpredictsacousticfeatureencodinginbothauditorymidbrainandcortex AT qingtianzhang hierarchicalsparsecodingmodelpredictsacousticfeatureencodinginbothauditorymidbrainandcortex AT xiaolinhu hierarchicalsparsecodingmodelpredictsacousticfeatureencodinginbothauditorymidbrainandcortex AT bohong hierarchicalsparsecodingmodelpredictsacousticfeatureencodinginbothauditorymidbrainandcortex AT bozhang hierarchicalsparsecodingmodelpredictsacousticfeatureencodinginbothauditorymidbrainandcortex |