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...
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Gespeichert in:

Autor*in:	Botros, S. M. [verfasserIn] Brace, E. N.

Format:	Artikel
Sprache:	Englisch

Erschienen:	1990

Schlagwörter:	Respiratory Cycle Stable Limit Cycle Stretch Receptor Respiratory Rhythm Neuronal Group

Anmerkung:	© Springer-Verlag 1990

Übergeordnetes Werk:	Enthalten in: Biological cybernetics - Springer-Verlag, 1975, 63(1990), 2 vom: Juni, Seite 143-153
Übergeordnetes Werk:	volume:63 ; year:1990 ; number:2 ; month:06 ; pages:143-153

Links:	Volltext

DOI / URN:	10.1007/BF00203037

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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allfields	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
allfields_unstemmed	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
language	English
source	Enthalten in Biological cybernetics 63(1990), 2 vom: Juni, Seite 143-153 volume:63 year:1990 number:2 month:06 pages:143-153
sourceStr	Enthalten in Biological cybernetics 63(1990), 2 vom: Juni, Seite 143-153 volume:63 year:1990 number:2 month:06 pages:143-153
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Respiratory Cycle Stable Limit Cycle Stretch Receptor Respiratory Rhythm Neuronal Group
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container_title	Biological cybernetics
authorswithroles_txt_mv	Botros, S. M. @@aut@@ Brace, E. N. @@aut@@
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author	Botros, S. M.
spellingShingle	Botros, S. M. ddc 570 ssgn 12 fid BIODIV misc Respiratory Cycle misc Stable Limit Cycle misc Stretch Receptor misc Respiratory Rhythm misc Neuronal Group Neural network implementation of a three-phase model of respiratory rhythm generation
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title	Neural network implementation of a three-phase model of respiratory rhythm generation
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title_full	Neural network implementation of a three-phase model of respiratory rhythm generation
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title_sort	neural network implementation of a three-phase model of respiratory rhythm generation
title_auth	Neural network implementation of a three-phase model of respiratory rhythm generation
abstract	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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title_short	Neural network implementation of a three-phase model of respiratory rhythm generation
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Neural network implementation of a three-phase model of respiratory rhythm generation

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