Hybrid Integrate-and-Fire Model of a Bursting Neuron

Breen, Barbara J.; Gerken, William Christopher; Butera, Robert J.

doi:10.1162/089976603322518768

Cited by 18 publications

(13 citation statements)

References 24 publications

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“…Neural circuit components were derived from previously described respiratory network models of discrete “IF” populations after MacGregor (1987) and a “hybrid IF burster” population with Hodgkin–Huxley style equations after Breen et al (2003). These models were developed iteratively with in vivo experiments that both guided model development and tested model predictions, as detailed in Rybak et al (2008) and Poliaček et al (2011).…”

Section: Methodsmentioning

confidence: 99%

A Joint Computational Respiratory Neural Network-Biomechanical Model for Breathing and Airway Defensive Behaviors

O'Connor¹,

Segers²,

Morris³

et al. 2012

Front. Physio.

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Data-driven computational neural network models have been used to study mechanisms for generating the motor patterns for breathing and breathing related behaviors such as coughing. These models have commonly been evaluated in open loop conditions or with feedback of lung volume simply represented as a filtered version of phrenic motor output. Limitations of these approaches preclude assessment of the influence of mechanical properties of the musculoskeletal system and motivated development of a biomechanical model of the respiratory muscles, airway, and lungs using published measures from human subjects. Here we describe the model and some aspects of its behavior when linked to a computational brainstem respiratory network model for breathing and airway defensive behavior composed of discrete "integrate and fire" populations. The network incorporated multiple circuit paths and operations for tuning inspiratory drive suggested by prior work. Results from neuromechanical system simulations included generation of a eupneic-like breathing pattern and the observation that increased respiratory drive and operating volume result in higher peak flow rates during cough, even when the expiratory drive is unchanged, or when the expiratory abdominal pressure is unchanged. Sequential elimination of the model's sources of inspiratory drive during cough also suggested a role for disinhibitory regulation via tonic expiratory neurons, a result that was subsequently supported by an analysis of in vivo data. Comparisons with antecedent models, discrepancies with experimental results, and some model limitations are noted.

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Section: Methodsmentioning

confidence: 99%

A Joint Computational Respiratory Neural Network-Biomechanical Model for Breathing and Airway Defensive Behaviors

O'Connor¹,

Segers²,

Morris³

et al. 2012

Front. Physio.

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“…This model used integrate-and-fire descriptions for both the nonpacemaker neurons (10) and I NaP -dependent pre-BötC bursters (25). In contrast to the Rybak et al (285) model, which included only a minimal set of pontine populations that did not interact with each other and had only hypothetical interconnections with the medullary populations, this model explicitly incorporated the types of pontine populations and their interconnections within the pons and with medullary populations that were identified by the analysis of many multielectrode recordings made in a series of coordinated in vivo studies in cats (297).…”

Section: Computational Models Of the Respiratory Cpgmentioning

confidence: 99%

Computational Models and Emergent Properties of Respiratory Neural Networks

Lindsey

Rybak

Smith

2012

Comprehensive Physiology

106

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Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered.

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“…The program includes the functionality of the program SYSTM11 [53] used in previous simulations of the respiratory network [37]. The simulated networks include both discrete “integrate and fire” (IF) populations with cell parameters similar to those in SYSTEM 11 and a “hybrid” IF conditional burster” population with parameters based on the model of Breen et al [54]. The latter population uses Hodgkin-Huxley style equations for subthreshold currents.…”

Section: Introductionmentioning

confidence: 99%

Role of the dorsal medulla in the neurogenesis of airway protection

Bolser

Pitts

Davenport

et al. 2015

Pulmonary Pharmacology & Therapeutics

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The dorsal medulla encompassing the nucleus of the tractus solitarius (NTS) and surrounding reticular formation (RF) has an important role in processing sensory information from the upper and lower airways for the generation and control of airway protective behaviors. These behaviors, such as cough and swallow, historically have been studied in isolation. However, recent information indicates that these and other airway protective behaviors are coordinated to minimize risk of aspiration. The dorsal medullary neural circuits that include the NTS are responsible for rhythmogenesis for repetitive swallowing, but previous models have assigned a role for this portion of the network for coughing that is restricted to monosynaptic sensory processing. We propose a more complex NTS/RF circuit that controls expression of swallowing and coughing and the coordination of these behaviors. The proposed circuit is supported by recordings of activity patterns of selected neural elements in vivo and simulations of a computational model of the brainstem circuit for breathing, coughing, and swallowing. This circuit includes separate rhythmic sub-circuits for all three behaviors. The revised NTS/RF circuit can account for the mode of action of antitussive drugs on the cough motor pattern, as well as the unique coordination of cough and swallow by a meta-behavioral control system for airway protection.

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Hybrid Integrate-and-Fire Model of a Bursting Neuron

Cited by 18 publications

References 24 publications

A Joint Computational Respiratory Neural Network-Biomechanical Model for Breathing and Airway Defensive Behaviors

A Joint Computational Respiratory Neural Network-Biomechanical Model for Breathing and Airway Defensive Behaviors

Computational Models and Emergent Properties of Respiratory Neural Networks

Role of the dorsal medulla in the neurogenesis of airway protection

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