Since Euler’s treatment of the celebrated “Seven Bridges of Königsberg” math puzzle as a network problem in 1735, graphs and networks have been used as model descriptions in virtually every facet of nature. Networks are particularly prevalent in biological systems. Neuronal network models have been the work horses of theoretical and computational neuroscientists for decades, not only to better understand biological neuronal networks, but also to draw inspiration for the analysis of experimental data and to help understand the function of the brain. Groundbreaking work by Erdós and Rényi laid the foundations to employ random networks as models. This has provided important insight into the dynamics of large networks in the brain. In fact, such models have served as a test bed for a variety of theoretical concepts. With recent advances in experimental techniques, however, it is becoming increasingly clear that the networks of the brain have statistical features that considerably deviate from classical random networks. Thus, the study of structured neuronal networks per se, and of the relations and constraints between structure, dynamics and function of networks, is rapidly developing into a new research paradigm in neuroscience.
This Research Topic of Frontiers in Computational Neuroscience is aimed at bringing together recent advances in the field of structured neuronal networks and the interactions between structure, dynamics and function of networks. We welcome contributions primarily from the field of neuroscience; however, contributions of general interest from related fields (e.g., communication networks, social networks, technical networks) will also be considered.
Topics of interest include but are not limited to:
Inferring structure from dynamics
Consequences of structure on dynamics
Structured networks and dynamics outside neuroscience, e.g., Internet/communication networks
Synaptic plasticity and emergence/loss of structure in the network
Consequence of non-randomness in the network on neural data analysis
Function and computing capabilities of structured networks
Artificially designed of non-random biological network in vitro
Since Euler’s treatment of the celebrated “Seven Bridges of Königsberg” math puzzle as a network problem in 1735, graphs and networks have been used as model descriptions in virtually every facet of nature. Networks are particularly prevalent in biological systems. Neuronal network models have been the work horses of theoretical and computational neuroscientists for decades, not only to better understand biological neuronal networks, but also to draw inspiration for the analysis of experimental data and to help understand the function of the brain. Groundbreaking work by Erdós and Rényi laid the foundations to employ random networks as models. This has provided important insight into the dynamics of large networks in the brain. In fact, such models have served as a test bed for a variety of theoretical concepts. With recent advances in experimental techniques, however, it is becoming increasingly clear that the networks of the brain have statistical features that considerably deviate from classical random networks. Thus, the study of structured neuronal networks per se, and of the relations and constraints between structure, dynamics and function of networks, is rapidly developing into a new research paradigm in neuroscience.
This Research Topic of Frontiers in Computational Neuroscience is aimed at bringing together recent advances in the field of structured neuronal networks and the interactions between structure, dynamics and function of networks. We welcome contributions primarily from the field of neuroscience; however, contributions of general interest from related fields (e.g., communication networks, social networks, technical networks) will also be considered.
Topics of interest include but are not limited to:
Inferring structure from dynamics
Consequences of structure on dynamics
Structured networks and dynamics outside neuroscience, e.g., Internet/communication networks
Synaptic plasticity and emergence/loss of structure in the network
Consequence of non-randomness in the network on neural data analysis
Function and computing capabilities of structured networks
Artificially designed of non-random biological network in vitro