Abstract
The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small () networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger () networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences.
Highlights
There is a great interest towards understanding the structure of neuronal networks, and the full connectome [1,2]
We show that the prediction of activity properties in networks with sharp in-degree distribution is best when clustering coefficient is used, whereas in networks with broad in-degree distribution the predictions based on maximum eigenvalue of the connectivity matrix are the most accurate
The reason to include loopy networks in this study is to address the question whether and to what extent such loops contribute to the dynamics in recurrent neuronal networks
Summary
There is a great interest towards understanding the structure of neuronal networks, and the full connectome [1,2]. The network structure lays a foundation to all collective activity observed in the system, and understanding this relationship is relevant both in vivo and in vitro. Promising experimental attempts have been made in controlling the growth of neurons to produce a pre-designed network structure [3,4]. If successful, such experiments would inform us on how the collective dynamics of the neurons is influenced by their patterns of synaptic connectivity. Such information is extremely challenging to obtain using the state-of-the-art equipment due to the complexity of processes involved in neuronal growth. Most of the nowadays studies on structure-function relationship in neuronal networks are likely to be conducted in silico, where the connectivity can be modified and the effect on the network dynamics instantaneously screened
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