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Impacts of clustering on noise-induced spiking regularity in the excitatory neuronal networks of subnetworks.

Li H, Sun X, Xiao J - Front Comput Neurosci (2015)

Bottom Line: With the obtained simulation results, we find that spiking regularity of the neuronal networks has little variations with changing of R and S when M is fixed.However, cluster number M could reduce the spiking regularity to low level when the uniform neuronal network's spiking regularity is at high level.Combined the obtained results, we can see that clustering factors have little influences on the spiking regularity when the entire energy is fixed, which could be controlled by the averaged coupling strength and the averaged connection probability.

View Article: PubMed Central - PubMed

Affiliation: School of Science, Beijing University of Posts and Telecommunications Beijing, China.

ABSTRACT
In this paper, we investigate how clustering factors influent spiking regularity of the neuronal network of subnetworks. In order to do so, we fix the averaged coupling probability and the averaged coupling strength, and take the cluster number M, the ratio of intra-connection probability and inter-connection probability R, the ratio of intra-coupling strength and inter-coupling strength S as controlled parameters. With the obtained simulation results, we find that spiking regularity of the neuronal networks has little variations with changing of R and S when M is fixed. However, cluster number M could reduce the spiking regularity to low level when the uniform neuronal network's spiking regularity is at high level. Combined the obtained results, we can see that clustering factors have little influences on the spiking regularity when the entire energy is fixed, which could be controlled by the averaged coupling strength and the averaged connection probability.

No MeSH data available.


Related in: MedlinePlus

Dependence of spiking regularity λ on the ratioRfor three different values of coupling strengthgwithM= 2 (A) andM= 4 (B). Here D = 0.015, S = 30. (colored on line).
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Figure 6: Dependence of spiking regularity λ on the ratioRfor three different values of coupling strengthgwithM= 2 (A) andM= 4 (B). Here D = 0.015, S = 30. (colored on line).

Mentions: Dependences of spiking regularity on the clustering factors R and S are presented by Figures 6, 7, respectively. In the Figures 6A, 7A, variations of λ on R and S for M = 2 are exhibited. From these two figures, we can see that λ just has small fluctuations when R and S changes for g = 0.004 and 0.01. When M = 2, for g = 0.025, combined with other clustering factors, the spiking regularity decreases with R quickly and then stays at a low level, as shown in Figure 6A; And the spiking regularity increases a little with S at first and then decreases to a low level, as shown in Figure 7A. When M = 4, we observe similar phenomena as M = 2, detailed simulation results are shown in Figures 6B, 7B. From these results, we can see that the clustering factors R and S has little influences on the spiking regularity. It means that for the current clustered neuronal networks, the averaged coupling strength g and the averaged connection probability p play the dominant role on controlling the spiking regularity. When we keep these two parameters being constants and just change the allocations between intra and inter connections, the spiking regularity of the whole clustered neuronal networks does not vary too much and is almost determined by values of parameters g and p.


Impacts of clustering on noise-induced spiking regularity in the excitatory neuronal networks of subnetworks.

Li H, Sun X, Xiao J - Front Comput Neurosci (2015)

Dependence of spiking regularity λ on the ratioRfor three different values of coupling strengthgwithM= 2 (A) andM= 4 (B). Here D = 0.015, S = 30. (colored on line).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493390&req=5

Figure 6: Dependence of spiking regularity λ on the ratioRfor three different values of coupling strengthgwithM= 2 (A) andM= 4 (B). Here D = 0.015, S = 30. (colored on line).
Mentions: Dependences of spiking regularity on the clustering factors R and S are presented by Figures 6, 7, respectively. In the Figures 6A, 7A, variations of λ on R and S for M = 2 are exhibited. From these two figures, we can see that λ just has small fluctuations when R and S changes for g = 0.004 and 0.01. When M = 2, for g = 0.025, combined with other clustering factors, the spiking regularity decreases with R quickly and then stays at a low level, as shown in Figure 6A; And the spiking regularity increases a little with S at first and then decreases to a low level, as shown in Figure 7A. When M = 4, we observe similar phenomena as M = 2, detailed simulation results are shown in Figures 6B, 7B. From these results, we can see that the clustering factors R and S has little influences on the spiking regularity. It means that for the current clustered neuronal networks, the averaged coupling strength g and the averaged connection probability p play the dominant role on controlling the spiking regularity. When we keep these two parameters being constants and just change the allocations between intra and inter connections, the spiking regularity of the whole clustered neuronal networks does not vary too much and is almost determined by values of parameters g and p.

Bottom Line: With the obtained simulation results, we find that spiking regularity of the neuronal networks has little variations with changing of R and S when M is fixed.However, cluster number M could reduce the spiking regularity to low level when the uniform neuronal network's spiking regularity is at high level.Combined the obtained results, we can see that clustering factors have little influences on the spiking regularity when the entire energy is fixed, which could be controlled by the averaged coupling strength and the averaged connection probability.

View Article: PubMed Central - PubMed

Affiliation: School of Science, Beijing University of Posts and Telecommunications Beijing, China.

ABSTRACT
In this paper, we investigate how clustering factors influent spiking regularity of the neuronal network of subnetworks. In order to do so, we fix the averaged coupling probability and the averaged coupling strength, and take the cluster number M, the ratio of intra-connection probability and inter-connection probability R, the ratio of intra-coupling strength and inter-coupling strength S as controlled parameters. With the obtained simulation results, we find that spiking regularity of the neuronal networks has little variations with changing of R and S when M is fixed. However, cluster number M could reduce the spiking regularity to low level when the uniform neuronal network's spiking regularity is at high level. Combined the obtained results, we can see that clustering factors have little influences on the spiking regularity when the entire energy is fixed, which could be controlled by the averaged coupling strength and the averaged connection probability.

No MeSH data available.


Related in: MedlinePlus