Neural network implementation of a three-phase model of respiratory rhythm generation
Abstract A mathematical model of the central neural mechanisms of respiratory rhythm generation is developed. This model assumes that the respiratory cycle consists of three phases: inspiration, post-inspiration, and expiration. Five respiratory neuronal groups are included: inspiratory, late-inspir...
Ausführliche Beschreibung
Autor*in: |
Botros, S. M. [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
1990 |
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Schlagwörter: |
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Anmerkung: |
© Springer-Verlag 1990 |
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Übergeordnetes Werk: |
Enthalten in: Biological cybernetics - Springer-Verlag, 1975, 63(1990), 2 vom: Juni, Seite 143-153 |
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Übergeordnetes Werk: |
volume:63 ; year:1990 ; number:2 ; month:06 ; pages:143-153 |
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DOI / URN: |
10.1007/BF00203037 |
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Katalog-ID: |
OLC2052690476 |
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520 | |a Abstract A mathematical model of the central neural mechanisms of respiratory rhythm generation is developed. This model assumes that the respiratory cycle consists of three phases: inspiration, post-inspiration, and expiration. Five respiratory neuronal groups are included: inspiratory, late-inspiratory, post-inspiratory, expiratory, and early-inspiratory neurons. Proposed interconnections among these groups are based substantially on previous physiological findings. The model produces a stable limit cycle and generally reproduces the features of the firing patterns of the 5 neuronal groups. When simulated feedback from pulmonary stretch receptors is made to excite late-inspiratory neurons and inhibit early-inspiratory neurons, the model quantitatively reproduces previous observations of the expiratory-prolonging effects of pulses and steps of vagal afferent activity presented in expiration. In addition the model reproduces expected respiratory cycle timing and amplitude responses to change of chemical drive both in the absence and in the presence of simulated stretch receptor feedback. These results demonstrate the feasibility of generating the respiratory rhythm with a simple neural network based on observed respiratory neuronal groups. Other neuronal groups not included in the model may be more important for shaping the waveforms than for generating the basic oscillation. | ||
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10.1007/BF00203037 doi (DE-627)OLC2052690476 (DE-He213)BF00203037-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Botros, S. M. verfasserin aut Neural network implementation of a three-phase model of respiratory rhythm generation 1990 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1990 Abstract A mathematical model of the central neural mechanisms of respiratory rhythm generation is developed. This model assumes that the respiratory cycle consists of three phases: inspiration, post-inspiration, and expiration. Five respiratory neuronal groups are included: inspiratory, late-inspiratory, post-inspiratory, expiratory, and early-inspiratory neurons. Proposed interconnections among these groups are based substantially on previous physiological findings. The model produces a stable limit cycle and generally reproduces the features of the firing patterns of the 5 neuronal groups. When simulated feedback from pulmonary stretch receptors is made to excite late-inspiratory neurons and inhibit early-inspiratory neurons, the model quantitatively reproduces previous observations of the expiratory-prolonging effects of pulses and steps of vagal afferent activity presented in expiration. In addition the model reproduces expected respiratory cycle timing and amplitude responses to change of chemical drive both in the absence and in the presence of simulated stretch receptor feedback. These results demonstrate the feasibility of generating the respiratory rhythm with a simple neural network based on observed respiratory neuronal groups. Other neuronal groups not included in the model may be more important for shaping the waveforms than for generating the basic oscillation. Respiratory Cycle Stable Limit Cycle Stretch Receptor Respiratory Rhythm Neuronal Group Brace, E. N. aut Enthalten in Biological cybernetics Springer-Verlag, 1975 63(1990), 2 vom: Juni, Seite 143-153 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:63 year:1990 number:2 month:06 pages:143-153 https://doi.org/10.1007/BF00203037 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_101 GBV_ILN_105 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2002 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2088 GBV_ILN_2237 GBV_ILN_2409 GBV_ILN_2410 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4082 GBV_ILN_4103 GBV_ILN_4193 GBV_ILN_4219 GBV_ILN_4302 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4310 GBV_ILN_4318 GBV_ILN_4324 GBV_ILN_4700 AR 63 1990 2 06 143-153 |
spelling |
10.1007/BF00203037 doi (DE-627)OLC2052690476 (DE-He213)BF00203037-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Botros, S. M. verfasserin aut Neural network implementation of a three-phase model of respiratory rhythm generation 1990 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1990 Abstract A mathematical model of the central neural mechanisms of respiratory rhythm generation is developed. This model assumes that the respiratory cycle consists of three phases: inspiration, post-inspiration, and expiration. Five respiratory neuronal groups are included: inspiratory, late-inspiratory, post-inspiratory, expiratory, and early-inspiratory neurons. Proposed interconnections among these groups are based substantially on previous physiological findings. The model produces a stable limit cycle and generally reproduces the features of the firing patterns of the 5 neuronal groups. When simulated feedback from pulmonary stretch receptors is made to excite late-inspiratory neurons and inhibit early-inspiratory neurons, the model quantitatively reproduces previous observations of the expiratory-prolonging effects of pulses and steps of vagal afferent activity presented in expiration. In addition the model reproduces expected respiratory cycle timing and amplitude responses to change of chemical drive both in the absence and in the presence of simulated stretch receptor feedback. These results demonstrate the feasibility of generating the respiratory rhythm with a simple neural network based on observed respiratory neuronal groups. Other neuronal groups not included in the model may be more important for shaping the waveforms than for generating the basic oscillation. Respiratory Cycle Stable Limit Cycle Stretch Receptor Respiratory Rhythm Neuronal Group Brace, E. N. aut Enthalten in Biological cybernetics Springer-Verlag, 1975 63(1990), 2 vom: Juni, Seite 143-153 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:63 year:1990 number:2 month:06 pages:143-153 https://doi.org/10.1007/BF00203037 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_101 GBV_ILN_105 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2002 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2088 GBV_ILN_2237 GBV_ILN_2409 GBV_ILN_2410 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4082 GBV_ILN_4103 GBV_ILN_4193 GBV_ILN_4219 GBV_ILN_4302 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4310 GBV_ILN_4318 GBV_ILN_4324 GBV_ILN_4700 AR 63 1990 2 06 143-153 |
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10.1007/BF00203037 doi (DE-627)OLC2052690476 (DE-He213)BF00203037-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Botros, S. M. verfasserin aut Neural network implementation of a three-phase model of respiratory rhythm generation 1990 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1990 Abstract A mathematical model of the central neural mechanisms of respiratory rhythm generation is developed. This model assumes that the respiratory cycle consists of three phases: inspiration, post-inspiration, and expiration. Five respiratory neuronal groups are included: inspiratory, late-inspiratory, post-inspiratory, expiratory, and early-inspiratory neurons. Proposed interconnections among these groups are based substantially on previous physiological findings. The model produces a stable limit cycle and generally reproduces the features of the firing patterns of the 5 neuronal groups. When simulated feedback from pulmonary stretch receptors is made to excite late-inspiratory neurons and inhibit early-inspiratory neurons, the model quantitatively reproduces previous observations of the expiratory-prolonging effects of pulses and steps of vagal afferent activity presented in expiration. In addition the model reproduces expected respiratory cycle timing and amplitude responses to change of chemical drive both in the absence and in the presence of simulated stretch receptor feedback. These results demonstrate the feasibility of generating the respiratory rhythm with a simple neural network based on observed respiratory neuronal groups. Other neuronal groups not included in the model may be more important for shaping the waveforms than for generating the basic oscillation. Respiratory Cycle Stable Limit Cycle Stretch Receptor Respiratory Rhythm Neuronal Group Brace, E. N. aut Enthalten in Biological cybernetics Springer-Verlag, 1975 63(1990), 2 vom: Juni, Seite 143-153 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:63 year:1990 number:2 month:06 pages:143-153 https://doi.org/10.1007/BF00203037 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_101 GBV_ILN_105 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2002 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2088 GBV_ILN_2237 GBV_ILN_2409 GBV_ILN_2410 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4082 GBV_ILN_4103 GBV_ILN_4193 GBV_ILN_4219 GBV_ILN_4302 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4310 GBV_ILN_4318 GBV_ILN_4324 GBV_ILN_4700 AR 63 1990 2 06 143-153 |
allfieldsGer |
10.1007/BF00203037 doi (DE-627)OLC2052690476 (DE-He213)BF00203037-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Botros, S. M. verfasserin aut Neural network implementation of a three-phase model of respiratory rhythm generation 1990 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1990 Abstract A mathematical model of the central neural mechanisms of respiratory rhythm generation is developed. This model assumes that the respiratory cycle consists of three phases: inspiration, post-inspiration, and expiration. Five respiratory neuronal groups are included: inspiratory, late-inspiratory, post-inspiratory, expiratory, and early-inspiratory neurons. Proposed interconnections among these groups are based substantially on previous physiological findings. The model produces a stable limit cycle and generally reproduces the features of the firing patterns of the 5 neuronal groups. When simulated feedback from pulmonary stretch receptors is made to excite late-inspiratory neurons and inhibit early-inspiratory neurons, the model quantitatively reproduces previous observations of the expiratory-prolonging effects of pulses and steps of vagal afferent activity presented in expiration. In addition the model reproduces expected respiratory cycle timing and amplitude responses to change of chemical drive both in the absence and in the presence of simulated stretch receptor feedback. These results demonstrate the feasibility of generating the respiratory rhythm with a simple neural network based on observed respiratory neuronal groups. Other neuronal groups not included in the model may be more important for shaping the waveforms than for generating the basic oscillation. Respiratory Cycle Stable Limit Cycle Stretch Receptor Respiratory Rhythm Neuronal Group Brace, E. N. aut Enthalten in Biological cybernetics Springer-Verlag, 1975 63(1990), 2 vom: Juni, Seite 143-153 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:63 year:1990 number:2 month:06 pages:143-153 https://doi.org/10.1007/BF00203037 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_101 GBV_ILN_105 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2002 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2088 GBV_ILN_2237 GBV_ILN_2409 GBV_ILN_2410 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4082 GBV_ILN_4103 GBV_ILN_4193 GBV_ILN_4219 GBV_ILN_4302 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4310 GBV_ILN_4318 GBV_ILN_4324 GBV_ILN_4700 AR 63 1990 2 06 143-153 |
allfieldsSound |
10.1007/BF00203037 doi (DE-627)OLC2052690476 (DE-He213)BF00203037-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Botros, S. M. verfasserin aut Neural network implementation of a three-phase model of respiratory rhythm generation 1990 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1990 Abstract A mathematical model of the central neural mechanisms of respiratory rhythm generation is developed. This model assumes that the respiratory cycle consists of three phases: inspiration, post-inspiration, and expiration. Five respiratory neuronal groups are included: inspiratory, late-inspiratory, post-inspiratory, expiratory, and early-inspiratory neurons. Proposed interconnections among these groups are based substantially on previous physiological findings. The model produces a stable limit cycle and generally reproduces the features of the firing patterns of the 5 neuronal groups. When simulated feedback from pulmonary stretch receptors is made to excite late-inspiratory neurons and inhibit early-inspiratory neurons, the model quantitatively reproduces previous observations of the expiratory-prolonging effects of pulses and steps of vagal afferent activity presented in expiration. In addition the model reproduces expected respiratory cycle timing and amplitude responses to change of chemical drive both in the absence and in the presence of simulated stretch receptor feedback. These results demonstrate the feasibility of generating the respiratory rhythm with a simple neural network based on observed respiratory neuronal groups. Other neuronal groups not included in the model may be more important for shaping the waveforms than for generating the basic oscillation. Respiratory Cycle Stable Limit Cycle Stretch Receptor Respiratory Rhythm Neuronal Group Brace, E. N. aut Enthalten in Biological cybernetics Springer-Verlag, 1975 63(1990), 2 vom: Juni, Seite 143-153 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:63 year:1990 number:2 month:06 pages:143-153 https://doi.org/10.1007/BF00203037 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_101 GBV_ILN_105 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2002 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2088 GBV_ILN_2237 GBV_ILN_2409 GBV_ILN_2410 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4046 GBV_ILN_4082 GBV_ILN_4103 GBV_ILN_4193 GBV_ILN_4219 GBV_ILN_4302 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4310 GBV_ILN_4318 GBV_ILN_4324 GBV_ILN_4700 AR 63 1990 2 06 143-153 |
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English |
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Enthalten in Biological cybernetics 63(1990), 2 vom: Juni, Seite 143-153 volume:63 year:1990 number:2 month:06 pages:143-153 |
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Enthalten in Biological cybernetics 63(1990), 2 vom: Juni, Seite 143-153 volume:63 year:1990 number:2 month:06 pages:143-153 |
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Abstract A mathematical model of the central neural mechanisms of respiratory rhythm generation is developed. This model assumes that the respiratory cycle consists of three phases: inspiration, post-inspiration, and expiration. Five respiratory neuronal groups are included: inspiratory, late-inspiratory, post-inspiratory, expiratory, and early-inspiratory neurons. Proposed interconnections among these groups are based substantially on previous physiological findings. The model produces a stable limit cycle and generally reproduces the features of the firing patterns of the 5 neuronal groups. When simulated feedback from pulmonary stretch receptors is made to excite late-inspiratory neurons and inhibit early-inspiratory neurons, the model quantitatively reproduces previous observations of the expiratory-prolonging effects of pulses and steps of vagal afferent activity presented in expiration. In addition the model reproduces expected respiratory cycle timing and amplitude responses to change of chemical drive both in the absence and in the presence of simulated stretch receptor feedback. These results demonstrate the feasibility of generating the respiratory rhythm with a simple neural network based on observed respiratory neuronal groups. Other neuronal groups not included in the model may be more important for shaping the waveforms than for generating the basic oscillation. © Springer-Verlag 1990 |
abstractGer |
Abstract A mathematical model of the central neural mechanisms of respiratory rhythm generation is developed. This model assumes that the respiratory cycle consists of three phases: inspiration, post-inspiration, and expiration. Five respiratory neuronal groups are included: inspiratory, late-inspiratory, post-inspiratory, expiratory, and early-inspiratory neurons. Proposed interconnections among these groups are based substantially on previous physiological findings. The model produces a stable limit cycle and generally reproduces the features of the firing patterns of the 5 neuronal groups. When simulated feedback from pulmonary stretch receptors is made to excite late-inspiratory neurons and inhibit early-inspiratory neurons, the model quantitatively reproduces previous observations of the expiratory-prolonging effects of pulses and steps of vagal afferent activity presented in expiration. In addition the model reproduces expected respiratory cycle timing and amplitude responses to change of chemical drive both in the absence and in the presence of simulated stretch receptor feedback. These results demonstrate the feasibility of generating the respiratory rhythm with a simple neural network based on observed respiratory neuronal groups. Other neuronal groups not included in the model may be more important for shaping the waveforms than for generating the basic oscillation. © Springer-Verlag 1990 |
abstract_unstemmed |
Abstract A mathematical model of the central neural mechanisms of respiratory rhythm generation is developed. This model assumes that the respiratory cycle consists of three phases: inspiration, post-inspiration, and expiration. Five respiratory neuronal groups are included: inspiratory, late-inspiratory, post-inspiratory, expiratory, and early-inspiratory neurons. Proposed interconnections among these groups are based substantially on previous physiological findings. The model produces a stable limit cycle and generally reproduces the features of the firing patterns of the 5 neuronal groups. When simulated feedback from pulmonary stretch receptors is made to excite late-inspiratory neurons and inhibit early-inspiratory neurons, the model quantitatively reproduces previous observations of the expiratory-prolonging effects of pulses and steps of vagal afferent activity presented in expiration. In addition the model reproduces expected respiratory cycle timing and amplitude responses to change of chemical drive both in the absence and in the presence of simulated stretch receptor feedback. These results demonstrate the feasibility of generating the respiratory rhythm with a simple neural network based on observed respiratory neuronal groups. Other neuronal groups not included in the model may be more important for shaping the waveforms than for generating the basic oscillation. © Springer-Verlag 1990 |
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score |
7.3994865 |