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The interaction of intrinsic dynamics and network topology in determining network burst synchrony.

Gaiteri C, Rubin JE - Front Comput Neurosci (2011)

Bottom Line: We find that several measures of network burst synchronization are determined by interactions of network topology with the intrinsic dynamics of neurons at central network positions and by the strengths of synaptic connections between neurons.Surprisingly, despite the functional role of synchronized bursting within the pre-BötC, we find that synchronized network bursting is generally weakest when we use its specific connection topology, which leads to synchrony within clusters but poor coordination across clusters.Overall, our results highlight the relevance of interactions between topology and intrinsic dynamics in shaping the activity of networks and the concerted effects of connectivity patterns and dynamic heterogeneities.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, University of Pittsburgh Pittsburgh, PA, USA.

ABSTRACT
The pre-Bötzinger complex (pre-BötC), within the mammalian respiratory brainstem, represents an ideal system for investigating the synchronization properties of complex neuronal circuits via the interaction of cell-type heterogeneity and network connectivity. In isolation, individual respiratory neurons from the pre-BötC may be tonically active, rhythmically bursting, or quiescent. Despite this intrinsic heterogeneity, coupled networks of pre-BötC neurons en bloc engage in synchronized bursting that can drive inspiratory motor neuron activation. The region's connection topology has been recently characterized and features dense clusters of cells with occasional connections between clusters. We investigate how the dynamics of individual neurons (quiescent/bursting/tonic) and the betweenness centrality of neurons' positions within the network connectivity graph interact to govern network burst synchrony, by simulating heterogeneous networks of computational model pre-BötC neurons. Furthermore, we compare the prevalence and synchrony of bursting across networks constructed with a variety of connection topologies, analyzing the same collection of heterogeneous neurons in small-world, scale-free, random, and regularly structured networks. We find that several measures of network burst synchronization are determined by interactions of network topology with the intrinsic dynamics of neurons at central network positions and by the strengths of synaptic connections between neurons. Surprisingly, despite the functional role of synchronized bursting within the pre-BötC, we find that synchronized network bursting is generally weakest when we use its specific connection topology, which leads to synchrony within clusters but poor coordination across clusters. Overall, our results highlight the relevance of interactions between topology and intrinsic dynamics in shaping the activity of networks and the concerted effects of connectivity patterns and dynamic heterogeneities.

No MeSH data available.


Related in: MedlinePlus

Interactions of synaptic coupling strength with topology and cell-type hierarchies. (A) Network synchrony (NBI) increases with increasing synaptic weight across topologies and cell-type hierarchies. (B) Increases in synchrony for different topologies under increasing synaptic weight. Each group of bars shows the NBI of different topologies for a sequence of increasing synaptic weights. (C) Demonstration that variability in NBI (indicated by error bars) is driven by cell hierarchies and not random fluctuations. Calculating the NBI for different networks based solely on simulations that used a random placement of cell types shows significantly smaller variation in network bursting than seen across all cell-type hierarchies in (B). (D) Within the broad increase in synchrony with increasing coupling, topology, and dynamics play divergent rolls. Green bars represent the number of significant differences between cell-type hierarchies for increasing levels of synaptic coupling. The trend in cell-type hierarchies shows the importance of cell-type placement on network activity declines with increasing synaptic weight. Conversely, the trend for distinctions between topologies (blue bars) shows that network architecture is responsible for a larger portion of variance in network synchrony at higher synaptic weights.
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Figure 5: Interactions of synaptic coupling strength with topology and cell-type hierarchies. (A) Network synchrony (NBI) increases with increasing synaptic weight across topologies and cell-type hierarchies. (B) Increases in synchrony for different topologies under increasing synaptic weight. Each group of bars shows the NBI of different topologies for a sequence of increasing synaptic weights. (C) Demonstration that variability in NBI (indicated by error bars) is driven by cell hierarchies and not random fluctuations. Calculating the NBI for different networks based solely on simulations that used a random placement of cell types shows significantly smaller variation in network bursting than seen across all cell-type hierarchies in (B). (D) Within the broad increase in synchrony with increasing coupling, topology, and dynamics play divergent rolls. Green bars represent the number of significant differences between cell-type hierarchies for increasing levels of synaptic coupling. The trend in cell-type hierarchies shows the importance of cell-type placement on network activity declines with increasing synaptic weight. Conversely, the trend for distinctions between topologies (blue bars) shows that network architecture is responsible for a larger portion of variance in network synchrony at higher synaptic weights.

Mentions: With no synaptic coupling, each topology consists of a collection of cells in which approximately one-third are quiescent, one-third are bursting, and one-third are tonically active. Increasing synapse weights changed the network activity and resulted in a significant increase in synchrony (p < 0.001, Figure 5A). This was accompanied by a significant increase in the number of bursting cells from the baseline 33 cells to an average of 75 cells among all topologies at the highest synapse weights.


The interaction of intrinsic dynamics and network topology in determining network burst synchrony.

Gaiteri C, Rubin JE - Front Comput Neurosci (2011)

Interactions of synaptic coupling strength with topology and cell-type hierarchies. (A) Network synchrony (NBI) increases with increasing synaptic weight across topologies and cell-type hierarchies. (B) Increases in synchrony for different topologies under increasing synaptic weight. Each group of bars shows the NBI of different topologies for a sequence of increasing synaptic weights. (C) Demonstration that variability in NBI (indicated by error bars) is driven by cell hierarchies and not random fluctuations. Calculating the NBI for different networks based solely on simulations that used a random placement of cell types shows significantly smaller variation in network bursting than seen across all cell-type hierarchies in (B). (D) Within the broad increase in synchrony with increasing coupling, topology, and dynamics play divergent rolls. Green bars represent the number of significant differences between cell-type hierarchies for increasing levels of synaptic coupling. The trend in cell-type hierarchies shows the importance of cell-type placement on network activity declines with increasing synaptic weight. Conversely, the trend for distinctions between topologies (blue bars) shows that network architecture is responsible for a larger portion of variance in network synchrony at higher synaptic weights.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3044261&req=5

Figure 5: Interactions of synaptic coupling strength with topology and cell-type hierarchies. (A) Network synchrony (NBI) increases with increasing synaptic weight across topologies and cell-type hierarchies. (B) Increases in synchrony for different topologies under increasing synaptic weight. Each group of bars shows the NBI of different topologies for a sequence of increasing synaptic weights. (C) Demonstration that variability in NBI (indicated by error bars) is driven by cell hierarchies and not random fluctuations. Calculating the NBI for different networks based solely on simulations that used a random placement of cell types shows significantly smaller variation in network bursting than seen across all cell-type hierarchies in (B). (D) Within the broad increase in synchrony with increasing coupling, topology, and dynamics play divergent rolls. Green bars represent the number of significant differences between cell-type hierarchies for increasing levels of synaptic coupling. The trend in cell-type hierarchies shows the importance of cell-type placement on network activity declines with increasing synaptic weight. Conversely, the trend for distinctions between topologies (blue bars) shows that network architecture is responsible for a larger portion of variance in network synchrony at higher synaptic weights.
Mentions: With no synaptic coupling, each topology consists of a collection of cells in which approximately one-third are quiescent, one-third are bursting, and one-third are tonically active. Increasing synapse weights changed the network activity and resulted in a significant increase in synchrony (p < 0.001, Figure 5A). This was accompanied by a significant increase in the number of bursting cells from the baseline 33 cells to an average of 75 cells among all topologies at the highest synapse weights.

Bottom Line: We find that several measures of network burst synchronization are determined by interactions of network topology with the intrinsic dynamics of neurons at central network positions and by the strengths of synaptic connections between neurons.Surprisingly, despite the functional role of synchronized bursting within the pre-BötC, we find that synchronized network bursting is generally weakest when we use its specific connection topology, which leads to synchrony within clusters but poor coordination across clusters.Overall, our results highlight the relevance of interactions between topology and intrinsic dynamics in shaping the activity of networks and the concerted effects of connectivity patterns and dynamic heterogeneities.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, University of Pittsburgh Pittsburgh, PA, USA.

ABSTRACT
The pre-Bötzinger complex (pre-BötC), within the mammalian respiratory brainstem, represents an ideal system for investigating the synchronization properties of complex neuronal circuits via the interaction of cell-type heterogeneity and network connectivity. In isolation, individual respiratory neurons from the pre-BötC may be tonically active, rhythmically bursting, or quiescent. Despite this intrinsic heterogeneity, coupled networks of pre-BötC neurons en bloc engage in synchronized bursting that can drive inspiratory motor neuron activation. The region's connection topology has been recently characterized and features dense clusters of cells with occasional connections between clusters. We investigate how the dynamics of individual neurons (quiescent/bursting/tonic) and the betweenness centrality of neurons' positions within the network connectivity graph interact to govern network burst synchrony, by simulating heterogeneous networks of computational model pre-BötC neurons. Furthermore, we compare the prevalence and synchrony of bursting across networks constructed with a variety of connection topologies, analyzing the same collection of heterogeneous neurons in small-world, scale-free, random, and regularly structured networks. We find that several measures of network burst synchronization are determined by interactions of network topology with the intrinsic dynamics of neurons at central network positions and by the strengths of synaptic connections between neurons. Surprisingly, despite the functional role of synchronized bursting within the pre-BötC, we find that synchronized network bursting is generally weakest when we use its specific connection topology, which leads to synchrony within clusters but poor coordination across clusters. Overall, our results highlight the relevance of interactions between topology and intrinsic dynamics in shaping the activity of networks and the concerted effects of connectivity patterns and dynamic heterogeneities.

No MeSH data available.


Related in: MedlinePlus