scholarly journals Cycle-by-cycle assembly of respiratory network activity is dynamic and stochastic

2013 ◽  
Vol 109 (2) ◽  
pp. 296-305 ◽  
Author(s):  
Michael S. Carroll ◽  
Jan-Marino Ramirez
Keyword(s):  
Computational Models ◽  
De Novo ◽  
Activity Patterns ◽  
Respiratory Rhythm ◽  
Network Models ◽  
Network Activity ◽  
Respiratory Neurons ◽  
Intrinsic Activity ◽  
Pacemaker Neurons ◽  
Network Output

Rhythmically active networks are typically composed of neurons that can be classified as silent, tonic spiking, or rhythmic bursting based on their intrinsic activity patterns. Within these networks, neurons are thought to discharge in distinct phase relationships with their overall network output, and it has been hypothesized that bursting pacemaker neurons may lead and potentially trigger cycle onsets. We used multielectrode recording from 72 experiments to test these ideas in rhythmically active slices containing the pre-Bötzinger complex, a region critical for breathing. Following synaptic blockade, respiratory neurons exhibited a gradient of intrinsic spiking to rhythmic bursting activities and thus defied an easy classification into bursting pacemaker and nonbursting categories. Features of their firing activity within the functional network were analyzed for correlation with subsequent rhythmic bursting in synaptic isolation. Higher firing rates through all phases of fictive respiration statistically predicted bursting pacemaker behavior. However, a cycle-by-cycle analysis indicated that respiratory neurons were stochastically activated with each burst. Intrinsically bursting pacemakers led some population bursts and followed others. This variability was not reproduced in traditional fully interconnected computational models, while sparsely connected network models reproduced these results both qualitatively and quantitatively. We hypothesize that pacemaker neurons do not act as clock-like drivers of the respiratory rhythm but rather play a flexible and dynamic role in the initiation and stabilization of each burst. Thus, at the behavioral level, each breath can be thought of as de novo assembly of a stochastic collaboration of network topology and intrinsic properties.

scholarly journals Imaging of respiratory-related population activity with single-cell resolution

2007 ◽  
Vol 292 (1) ◽  
pp. C508-C516 ◽  
Author(s):  
Frank Funke ◽  
Mathias Dutschmann ◽  
Michael Müller
Keyword(s):  
Single Cell ◽  
Neuronal Activity ◽  
Single Cells ◽  
Activity Patterns ◽  
Respiratory Rhythm ◽  
Network Activity ◽  
Respiratory Neurons ◽  
Ventrolateral Medulla ◽  
Population Activity ◽  
Respiratory Network

The pre-Bötzinger complex (PBC) in the rostral ventrolateral medulla contains a kernel involved in respiratory rhythm generation. So far, its respiratory activity has been analyzed predominantly by electrophysiological approaches. Recent advances in fluorescence imaging now allow for the visualization of neuronal population activity in rhythmogenic networks. In the respiratory network, voltage-sensitive dyes have been used mainly, so far, but their low sensitivity prevents an analysis of activity patterns of single neurons during rhythmogenesis. We now have succeeded in using more sensitive Ca2+ imaging to study respiratory neurons in rhythmically active brain stem slices of neonatal rats. For the visualization of neuronal activity, fluo-3 was suited best in terms of neuronal specificity, minimized background fluorescence, and response magnitude. The tissue penetration of fluo-3 was improved by hyperosmolar treatment (100 mM mannitol) during dye loading. Rhythmic population activity was imaged with single-cell resolution using a sensitive charge-coupled device camera and a ×20 objective, and it was correlated with extracellularly recorded mass activity of the contralateral PBC. Correlated optical neuronal activity was obvious online in 29% of slices. Rhythmic neurons located deeper became detectable during offline image processing. Based on their activity patterns, 74% of rhythmic neurons were classified as inspiratory and 26% as expiratory neurons. Our approach is well suited to visualize and correlate the activity of several single cells with respiratory network activity. We demonstrate that neuronal synchronization and possibly even network configurations can be analyzed in a noninvasive approach with single-cell resolution and at frame rates currently not reached by most scanning-based imaging techniques.

scholarly journals Reconfiguration of the Pontomedullary Respiratory Network: A Computational Modeling Study With Coordinated In Vivo Experiments

2008 ◽  
Vol 100 (4) ◽  
pp. 1770-1799 ◽  
Author(s):  
I. A. Rybak ◽  
R. O'Connor ◽  
A. Ross ◽  
N. A. Shevtsova ◽  
S. C. Nuding ◽  
...  
Keyword(s):  
Computational Models ◽  
Ad Hoc ◽  
Large Body ◽  
Network Activity ◽  
Respiratory Pattern ◽  
Respiratory Neurons ◽  
Phase Switching ◽  
In Vivo Experiments ◽  
Respiratory Network

A large body of data suggests that the pontine respiratory group (PRG) is involved in respiratory phase-switching and the reconfiguration of the brain stem respiratory network. However, connectivity between the PRG and ventral respiratory column (VRC) in computational models has been largely ad hoc. We developed a network model with PRG-VRC connectivity inferred from coordinated in vivo experiments. Neurons were modeled in the “integrate-and-fire” style; some neurons had pacemaker properties derived from the model of Breen et al. We recapitulated earlier modeling results, including reproduction of activity profiles of different respiratory neurons and motor outputs, and their changes under different conditions (vagotomy, pontine lesions, etc.). The model also reproduced characteristic changes in neuronal and motor patterns observed in vivo during fictive cough and during hypoxia in non-rapid eye movement sleep. Our simulations suggested possible mechanisms for respiratory pattern reorganization during these behaviors. The model predicted that network- and pacemaker-generated rhythms could be co-expressed during the transition from gasping to eupnea, producing a combined “burst-ramp” pattern of phrenic discharges. To test this prediction, phrenic activity and multiple single neuron spike trains were monitored in vagotomized, decerebrate, immobilized, thoracotomized, and artificially ventilated cats during hypoxia and recovery. In most experiments, phrenic discharge patterns during recovery from hypoxia were similar to those predicted by the model. We conclude that under certain conditions, e.g., during recovery from severe brain hypoxia, components of a distributed network activity present during eupnea can be co-expressed with gasp patterns generated by a distinct, functionally “simplified” mechanism.

Functional Imaging Reveals Respiratory Network Activity During Hypoxic and Opioid Challenge in the Neonate Rat Tilted Sagittal Slab Preparation

2007 ◽  
Vol 97 (3) ◽  
pp. 2283-2292 ◽  
Author(s):  
Benjamin J. Barnes ◽  
Chi-Minh Tuong ◽  
Nicholas M. Mellen
Keyword(s):  
Functional Imaging ◽  
Respiratory Rhythm ◽  
Network Activity ◽  
Respiratory Neurons ◽  
Respiratory Network ◽  
Burst Amplitude ◽  
Single Unit Recordings ◽  
Parafacial Respiratory Group ◽  
Neonate Rat ◽  
Respiratory Networks

In mammals, respiration-modulated networks are distributed rostrocaudally in the ventrolateral quadrant of the medulla. Recent studies have established that in neonate rodents, two spatially separate networks along this column—the parafacial respiratory group (pFRG) and the pre-Bötzinger complex (preBötC)—are hypothesized to be sufficient for respiratory rhythm generation, but little is known about the connectivity within or between these networks. To be able to observe how these networks interact, we have developed a neonate rat medullary tilted sagittal slab, which exposes one column of respiration-modulated neurons on its surface, permitting functional imaging with cellular resolution. Here we examined how respiratory networks responded to hypoxic challenge and opioid-induced depression. At the systems level, the sagittal slab was congruent with more intact preparations: hypoxic challenge led to a significant increase in respiratory period and inspiratory burst amplitude, consistent with gasping. At opioid concentrations sufficient to slow respiration, we observed periods at integer multiples of control, matching quantal slowing. Consistent with single-unit recordings in more intact preparations, respiratory networks were distributed bimodally along the rostrocaudal axis, with respiratory neurons concentrated at the caudal pole of the facial nucleus, and 350 microns caudally, at the level of the pFRG and the preBötC, respectively. Within these regions neurons active during hypoxia- and/or opioid-induced depression were ubiquitous and interdigitated. In particular, contrary to earlier reports, opiate-insensitive neurons were found at the level of the preBötC.

Identification of Two Types of Inspiratory Pacemaker Neurons in the Isolated Respiratory Neural Network of Mice

2001 ◽  
Vol 86 (1) ◽  
pp. 104-112 ◽  
Author(s):  
Muriel Thoby-Brisson ◽  
Jan-Marino Ramirez
Keyword(s):  
Neural Network ◽  
Respiratory Rhythm ◽  
Network Activity ◽  
Pacemaker Activity ◽  
Patch Clamp Technique ◽  
Population Activity ◽  
Pacemaker Neurons ◽  
Whole Cell Patch Clamp ◽  
Respiratory Network ◽  
Bursting Activity

In the respiratory network of mice, we characterized with the whole cell patch-clamp technique pacemaker properties in neurons discharging in phase with inspiration. The respiratory network was isolated in a transverse brain stem slice containing the pre-Bötzinger complex (PBC), the presumed site for respiratory rhythm generation. After blockade of respiratory network activity with 6-cyano-7-nitroquinoxalene-2,3-dione (CNQX), 18 of 52 inspiratory neurons exhibited endogenous pacemaker activity, which was voltage dependent, could be reset by brief current injections and could be entrained by repetitive stimuli. In the pacemaker group ( n = 18), eight neurons generated brief bursts (0.43 ± 0.03 s) at a relatively high frequency (1.05 ± 0.12 Hz) in CNQX. These bursts resembled the bursts that these neurons generated in the intact network during the interval between two inspiratory bursts. Cadmium (200 μM) altered but did not eliminate this bursting activity, while 0.5 μM tetrodotoxin suppressed bursting activity. Another set of pacemaker neurons (10 of 18) generated in CNQX longer bursts (1.57 ± 0.07 s) at a lower frequency (0.35 ± 0.01 Hz). These bursts resembled the inspiratory bursts generated in the intact network in phase with the population activity. This bursting activity was blocked by 50–100 μM cadmium or 0.5 μM tetrodotoxin. We conclude that the respiratory neural network contains pacemaker neurons with two types of bursting properties. The two types of pacemaker activities might have different functions within the respiratory network.

scholarly journals Mechanisms Underlying Adaptation of Respiratory Network Activity to Modulatory Stimuli in the Mouse Embryo

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Marc Chevalier ◽  
Rafaël De Sa ◽  
Laura Cardoit ◽  
Muriel Thoby-Brisson
Keyword(s):  
Ph Regulation ◽  
Respiratory Rhythm ◽  
Network Activity ◽  
Pacemaker Neurons ◽  
Functional Flexibility ◽  
Respiratory Rhythmogenesis ◽  
Brainstem Slice ◽  
Respiratory Network ◽  
The Impact

Breathing is a rhythmic behavior that requires organized contractions of respiratory effector muscles. This behavior must adapt to constantly changing conditions in order to ensure homeostasis, proper body oxygenation, and CO2/pH regulation. Respiratory rhythmogenesis is controlled by neural networks located in the brainstem. One area considered to be essential for generating the inspiratory phase of the respiratory rhythm is the preBötzinger complex (preBötC). Rhythmogenesis emerges from this network through the interplay between the activation of intrinsic cellular properties (pacemaker properties) and intercellular synaptic connections. Respiratory activity continuously changes under the impact of numerous modulatory substances depending on organismal needs and environmental conditions. The preBötC network has been shown to become active during the last third of gestation. But only little is known regarding the modulation of inspiratory rhythmicity at embryonic stages and even less on a possible role of pacemaker neurons in this functional flexibility during the prenatal period. By combining electrophysiology and calcium imaging performed on embryonic brainstem slice preparations, we provide evidence showing that embryonic inspiratory pacemaker neurons are already intrinsically sensitive to neuromodulation and external conditions (i.e., temperature) affecting respiratory network activity, suggesting a potential role of pacemaker neurons in mediating rhythm adaptation to modulatory stimuli in the embryo.

Norepinephrine Differentially Modulates Different Types of Respiratory Pacemaker and Nonpacemaker Neurons

2006 ◽  
Vol 95 (4) ◽  
pp. 2070-2082 ◽  
Author(s):  
Jean-Charles Viemari ◽  
Jan-Marino Ramirez
Keyword(s):  
Frequency Modulation ◽  
Respiratory Rhythm ◽  
Network Activity ◽  
Flufenamic Acid ◽  
Pacemaker Activity ◽  
Rhythm Generation ◽  
Burst Frequency ◽  
Pacemaker Neurons ◽  
Respiratory Network ◽  
Different Types

Pacemakers are found throughout the mammalian CNS. Yet, it remains largely unknown how these neurons contribute to network activity. Here we show that for the respiratory network isolated in transverse slices of mice, different functions can be assigned to different types of pacemakers and nonpacemakers. This difference becomes evident in response to norepinephrine (NE). Although NE depolarized 88% of synaptically isolated inspiratory neurons, this neuromodulator had differential effects on different neuron types. NE increased in cadmium-insensitive pacemakers burst frequency, not burst area and duration, and it increased in cadmium-sensitive pacemakers burst duration and area, but not frequency. NE also differentially modulated nonpacemakers. Two types of nonpacemakers were identified: “silent nonpacemakers” stop spiking, whereas “active nonpacemakers” spontaneously spike when isolated from the network. NE selectively induced cadmium-sensitive pacemaker properties in active, but not silent, nonpacemakers. Flufenamic acid (FFA), a blocker of ICAN, blocked the induction as well as modulation of cadmium-sensitive pacemaker activity, and blocked at the network level the NE-induced increase in burst area and duration of inspiratory network activity; the frequency modulation (FM) was unaffected. We therefore propose that modulation of cadmium-sensitive pacemaker activity contributes at the network level to changes in burst shape, not frequency. Riluzole blocked the FM of isolated cadmium-insensitive pacemakers. In the presence of riluzole, NE caused disorganized network activity, suggesting that cadmium-insensitive pacemakers are critical for rhythm generation. We conclude that different types of nonpacemaker and pacemaker neurons differentially control different aspects of the respiratory rhythm.

Modeling Neural Mechanisms for Genesis of Respiratory Rhythm and Pattern. II. Network Models of the Central Respiratory Pattern Generator

1997 ◽  
Vol 77 (4) ◽  
pp. 2007-2026 ◽  
Author(s):  
Ilya A. Rybak ◽  
Julian F. R. Paton ◽  
James S. Schwaber
Keyword(s):  
Respiratory Rhythm ◽  
Network Models ◽  
Pattern Generator ◽  
Respiratory Pattern ◽  
Respiratory Neurons ◽  
Two Phase ◽  
Firing Patterns ◽  
Three Phase ◽  
Phase Models ◽  
Respiratory Phases

Rybak, Ilya A., Julian F. R. Paton, and James S. Schwaber. Modeling neural mechanisms for genesis of respiratory rhythm and pattern. II. Network models of the central respiratory pattern generator. J. Neurophysiol. 77: 2007–2026, 1997. The present paper describes several models of the central respiratory pattern generator (CRPG) developed employing experimental data and current hypotheses for respiratory rhythmogenesis. Each CRPG model includes a network of respiratory neuron types (e.g., early inspiratory; ramp inspiratory; late inspiratory; decrementing expiratory; postinspiratory; stage II expiratory; stage II constant firing expiratory; preinspiratory) and simplified models of lung and pulmonary stretch receptors (PSR), which provide feedback to the respiratory network. The used models of single respiratory neurons were developed in the Hodgkin-Huxley style as described in the previous paper. The mechanism for termination of inspiration (the inspiratory off-switch) in all models operates via late-I neuron, which is considered to be the inspiratory off-switching neuron. Several two- and three-phase CRPG models have been developed using different accepted hypotheses of the mechanism for termination of expiration. The key elements in the two-phase models are the early-I and dec-E neurons. The expiratory off-switch mechanism in these models is based on the mutual inhibitory connections between early-I and dec-E and adaptive properties of the dec-E neuron. The difference between the two-phase models concerns the mechanism for ramp firing patterns of E2 neurons resulting either from the intrinsic neuronal properties of the E2 neuron or from disinhibition from the adapting dec-E neuron. The key element of the three-phase models is the pre-I neuron, which acts as the expiratory off-switching neuron. The three-phase models differ by the mechanisms used for termination of expiration and for the ramp firing patterns of E2 neurons. Additional CRPG models were developed employing a dual switching neuron that generates two bursts per respiratory cycle to terminate both inspiration and expiration. Although distinctly different each model generates a stable respiratory rhythm and shows physiologically plausible firing patterns of respiratory neurons with and without PSR feedback. Using our models, we analyze the roles of different respiratory neuron types and their interconnections for the respiratory rhythm and pattern generation. We also investigate the possible roles of intrinsic biophysical properties of different respiratory neurons in controlling the duration of respiratory phases and timing of switching between them. We show that intrinsic membrane properties of respiratory neurons are integrated with network properties of the CRPG at three hierarchical levels: at the cellular level to provide the specific firing patterns of respiratory neurons (e.g., ramp firing patterns); at the network level to provide switching between the respiratory phases; and at the systems level to control the duration of inspiration and expiration under different conditions (e.g., lack of PSR feedback).

scholarly journals Simulation of Spontaneous Activity in Neuronal Cultures with Long-Term Plasticity

2015 ◽  
Vol 10 (1) ◽  
pp. 234-244 ◽  
Author(s):  
А.А. Дегтерев ◽  
A.A. Degterev
Keyword(s):  
Spontaneous Activity ◽  
Network Models ◽  
Network Activity ◽  
Neuronal Cultures ◽  
Pacemaker Neurons ◽  
Short Term Plasticity ◽  
Synaptic Noise ◽  
Stdp Rule

Existence of spontaneous population bursts is a widely studied phenomenon observed in neuronal cultures in vitro. Recent models of neuronal cultures network activity consist of a number of burst generating mechanisms such as synaptic noise and presence of pacemaker neurons in the network. In the previous simulations of bursting in neuronal cultures synaptic weights change in accordance with the rule of short-term plasticity whereas the long-term values of them, and hence the network structure, remain unchanged. In this paper we reproduce neuronal network models with static synapses, and then investigate spontaneous activity changes in neuronal networks with long-term plasticity defined by STDP rule. Our results demonstrate that introduction of long-term plasticity in the model leads to discrepancy with the experimental data.

scholarly journals NDDN: A Cloud-Based Neuroinformation Database for Developing Neuronal Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Jiangbo Pu ◽  
Xiangning Li
Keyword(s):  
Neuronal Networks ◽  
Computational Models ◽  
Activity Patterns ◽  
Drug Application ◽  
Network Activity ◽  
Neural Information ◽  
Neural Information Processing ◽  
Processing And Storage ◽  
Hippocampal Networks

Electrical activity of developing dissociated neuronal networks is of immense significance for understanding the general properties of neural information processing and storage. In addition, the complexity and diversity of network activity patterns make them ideal candidates for developing novel computational models and evaluating algorithms. However, there are rare databases which focus on the changing network dynamics during development. Here, we describe the design and implementation of Neuroinformation Database for Developing Networks (NDDN), a repository for electrophysiological data collected from long-term cultured hippocampal networks. The NDDN contains over 15 terabytes of multielectrode array data consisting of 25,380 items collected from 105 culture batches. Metadata including culturing and recording information and stimulation/drug application protocols are linked to each data item. A Matlab toolbox named MEAKit is also provided with the NDDN to ease the analysis of downloaded data items. We expect that NDDN may contribute to both the fields of experimental and computational neuroscience.

scholarly journals Disparate energy consumption despite similar network activity

2021 ◽  
Author(s):  
Michael Deistler ◽  
Jakob H Macke ◽  
Pedro J Goncalves
Keyword(s):  
Energy Consumption ◽  
Neural Circuits ◽  
Specific Activity ◽  
Activity Patterns ◽  
Network Models ◽  
Metabolic Cost ◽  
Network Activity ◽  
Metabolic Efficiency ◽  
Cancer Borealis ◽  
Similar Activity

Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here, we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis. We use a novel machine learning method to identify a range of network models that can generate activity patterns matching experimental data. We find that neural circuits can consume largely different amounts of energy despite similar circuit activity. We then study how circuit parameters get constrained by minimizing energy consumption and identify circuit parameters that might be subject to metabolic tuning. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures, but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that, despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances.

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