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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.


Schematic presentation of the considered network architecture. The whole network consists of M = 4 subnetworks, each containing 10 neurons. Neurons inside each subnetwork connected with each other with probability pin and neurons from different subnetworks connected with each with probability pout. Here the ratio R = pin∕pout is taken as 15 and the connections probability between all neuron inside the whole network is taken as 0.15.
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Figure 1: Schematic presentation of the considered network architecture. The whole network consists of M = 4 subnetworks, each containing 10 neurons. Neurons inside each subnetwork connected with each other with probability pin and neurons from different subnetworks connected with each with probability pout. Here the ratio R = pin∕pout is taken as 15 and the connections probability between all neuron inside the whole network is taken as 0.15.

Mentions: In this paper, neuronal network is considered to have clustered structure. It totally contains N excitatory neurons, which are divided into M subnetworks equally. Namely, there are N∕M neurons in each subnetwork. Here we set N = 200. Neurons inside the network are linked randomly. Neuron pairs in the same subnetwork are linked with the probability pin, while neuron pairs from different subnetworks are linked with the probability pout. Higher values of R = pin∕pout favor connections within a local subnetwork over non-local connections. In this paper, the quantities pin and pout are chosen so that the connection probability between excitatory neurons remained 0.05 when averaged across all pairs (Kumar-Litwin and Doiron, 2012). pin and pout are named as intra-connection probability and inter-connection probability here, respectively. In Figure 1, an example of the considered network topology is shown. In this figure, which serves illustrative purposes, there are four subnetworks, each consisting of 10 neurons. Neurons inside each subnetwork connected with each other with the probability pin and neurons from different subnetworks connected with each with the probability pout. Here the ratio R = pin∕pout is taken as 15 and the connections probability between all neuron inside the whole network is taken as 0.15. In Figure 1, the connections inside subnetworks are thicker than the connections between different subnetworks. This indicates that the coupling strength of neurons inside subnetworks is larger than the coupling strength of neurons between different subnetworks, which will be illustrated in details in the following contents.


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)

Schematic presentation of the considered network architecture. The whole network consists of M = 4 subnetworks, each containing 10 neurons. Neurons inside each subnetwork connected with each other with probability pin and neurons from different subnetworks connected with each with probability pout. Here the ratio R = pin∕pout is taken as 15 and the connections probability between all neuron inside the whole network is taken as 0.15.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Schematic presentation of the considered network architecture. The whole network consists of M = 4 subnetworks, each containing 10 neurons. Neurons inside each subnetwork connected with each other with probability pin and neurons from different subnetworks connected with each with probability pout. Here the ratio R = pin∕pout is taken as 15 and the connections probability between all neuron inside the whole network is taken as 0.15.
Mentions: In this paper, neuronal network is considered to have clustered structure. It totally contains N excitatory neurons, which are divided into M subnetworks equally. Namely, there are N∕M neurons in each subnetwork. Here we set N = 200. Neurons inside the network are linked randomly. Neuron pairs in the same subnetwork are linked with the probability pin, while neuron pairs from different subnetworks are linked with the probability pout. Higher values of R = pin∕pout favor connections within a local subnetwork over non-local connections. In this paper, the quantities pin and pout are chosen so that the connection probability between excitatory neurons remained 0.05 when averaged across all pairs (Kumar-Litwin and Doiron, 2012). pin and pout are named as intra-connection probability and inter-connection probability here, respectively. In Figure 1, an example of the considered network topology is shown. In this figure, which serves illustrative purposes, there are four subnetworks, each consisting of 10 neurons. Neurons inside each subnetwork connected with each other with the probability pin and neurons from different subnetworks connected with each with the probability pout. Here the ratio R = pin∕pout is taken as 15 and the connections probability between all neuron inside the whole network is taken as 0.15. In Figure 1, the connections inside subnetworks are thicker than the connections between different subnetworks. This indicates that the coupling strength of neurons inside subnetworks is larger than the coupling strength of neurons between different subnetworks, which will be illustrated in details in the following contents.

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.