A circular model for song motor control inserinus canaria
Song production in songbirds is controlled by a network of nuclei distributed across several brain regions, which drives respiratory and vocal motor systems to generate sound. We built a model for birdsong production, whose variables are the average activities of different neural populations within...
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2015
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16625188_v9_nAPR_p_Alonso http://hdl.handle.net/20.500.12110/paper_16625188_v9_nAPR_p_Alonso |
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paper:paper_16625188_v9_nAPR_p_Alonso |
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paper:paper_16625188_v9_nAPR_p_Alonso2023-06-08T16:25:53Z A circular model for song motor control inserinus canaria Birdsong Motor control Non-linear dynamics Rate models Song system Brain Forecasting Neural networks Respirators Birdsong Motor control Non-linear dynamics Rate models Song system Independent component analysis adult Article controlled study expiratory related neuron male motor control nerve cell nerve projection neuromuscular function nonhuman prediction Serinus Serinus canaria singing telencephalon vocalization Song production in songbirds is controlled by a network of nuclei distributed across several brain regions, which drives respiratory and vocal motor systems to generate sound. We built a model for birdsong production, whose variables are the average activities of different neural populations within these nuclei of the song system. We focus on the predictions of respiratory patterns of song, because these can be easily measured and therefore provide a validation for the model. We test the hypothesis that it is possible to construct a model in which (1) the activity of an expiratory related (ER) neural population fits the observed pressure patterns used by canaries during singing, and (2) a higher forebrain neural population, HVC, is sparsely active, simultaneously with significant motor instances of the pressure patterns. We show that in order to achieve these two requirements, the ER neural population needs to receive two inputs: a direct one, and its copy after being processed by other areas of the song system. The model is capable of reproducing the measured respiratory patterns and makes specific predictions on the timing of HVC activity during their production. These results suggest that vocal production is controlled by a circular network rather than by a simple top-down architecture. © 2015, Johns Hopkins University Press. All rights reserved. 2015 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16625188_v9_nAPR_p_Alonso http://hdl.handle.net/20.500.12110/paper_16625188_v9_nAPR_p_Alonso |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Birdsong Motor control Non-linear dynamics Rate models Song system Brain Forecasting Neural networks Respirators Birdsong Motor control Non-linear dynamics Rate models Song system Independent component analysis adult Article controlled study expiratory related neuron male motor control nerve cell nerve projection neuromuscular function nonhuman prediction Serinus Serinus canaria singing telencephalon vocalization |
| spellingShingle |
Birdsong Motor control Non-linear dynamics Rate models Song system Brain Forecasting Neural networks Respirators Birdsong Motor control Non-linear dynamics Rate models Song system Independent component analysis adult Article controlled study expiratory related neuron male motor control nerve cell nerve projection neuromuscular function nonhuman prediction Serinus Serinus canaria singing telencephalon vocalization A circular model for song motor control inserinus canaria |
| topic_facet |
Birdsong Motor control Non-linear dynamics Rate models Song system Brain Forecasting Neural networks Respirators Birdsong Motor control Non-linear dynamics Rate models Song system Independent component analysis adult Article controlled study expiratory related neuron male motor control nerve cell nerve projection neuromuscular function nonhuman prediction Serinus Serinus canaria singing telencephalon vocalization |
| description |
Song production in songbirds is controlled by a network of nuclei distributed across several brain regions, which drives respiratory and vocal motor systems to generate sound. We built a model for birdsong production, whose variables are the average activities of different neural populations within these nuclei of the song system. We focus on the predictions of respiratory patterns of song, because these can be easily measured and therefore provide a validation for the model. We test the hypothesis that it is possible to construct a model in which (1) the activity of an expiratory related (ER) neural population fits the observed pressure patterns used by canaries during singing, and (2) a higher forebrain neural population, HVC, is sparsely active, simultaneously with significant motor instances of the pressure patterns. We show that in order to achieve these two requirements, the ER neural population needs to receive two inputs: a direct one, and its copy after being processed by other areas of the song system. The model is capable of reproducing the measured respiratory patterns and makes specific predictions on the timing of HVC activity during their production. These results suggest that vocal production is controlled by a circular network rather than by a simple top-down architecture. © 2015, Johns Hopkins University Press. All rights reserved. |
| title |
A circular model for song motor control inserinus canaria |
| title_short |
A circular model for song motor control inserinus canaria |
| title_full |
A circular model for song motor control inserinus canaria |
| title_fullStr |
A circular model for song motor control inserinus canaria |
| title_full_unstemmed |
A circular model for song motor control inserinus canaria |
| title_sort |
circular model for song motor control inserinus canaria |
| publishDate |
2015 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_16625188_v9_nAPR_p_Alonso http://hdl.handle.net/20.500.12110/paper_16625188_v9_nAPR_p_Alonso |
| _version_ |
1768543250629525504 |