ArticlePLoS computational biology2016
Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- Modeling neuron-astrocyte interactions in neural networks using distributed simulation.PLoS computational biology · 2025Article
- Spiking dynamics of individual neurons reflect changes in the structure and function of neuronal networks.Nature communications · 2025Article
- Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity.Scientific reports · 2023Article
- Stochastic bursting in networks of excitable units with delayed coupling.Biological cybernetics · 2022Article
- NewFrontiers in physiology · 2022Article
- Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks.PLoS computational biology · 2020Article
- Autonomous emergence of connectivity assemblies via spike triplet interactions.PLoS computational biology · 2020Article
- Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience.Current opinion in neurobiology · 2019Review
- Training and Spontaneous Reinforcement of Neuronal Assemblies by Spike Timing Plasticity.Cerebral cortex (New York, N.Y. : 1991) · 2019Article
- Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models.PLoS computational biology · 2017Article
- A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system.PLoS computational biology · 2017Article
- From the statistics of connectivity to the statistics of spike times in neuronal networks.Current opinion in neurobiology · 2017Review
- Spatiotemporal dynamics in excitable homogeneous random networks composed of periodically self-sustained oscillation.Scientific reports · 2017Article
- Linking structure and activity in nonlinear spiking networks.PLoS computational biology · 2017Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology-random networks of Erdős-Rényi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.
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