title: Fast sparsely synchronized brain rhythms in a scale-free neural network.
abstract: We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D<D*, population synchronization emerges in the whole population because the spatial correlation length between the neuronal pairs covers the whole system. Furthermore, the degree of population synchronization is also measured in terms of two types of realistic statistical-mechanical measures. Only for the partial and sparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of statistically homogeneous random graphs and small-world networks. Finally, we investigate the effect of network architecture on sparse synchronization for fixed values of J and D in the following three cases: (1) variation in the degree of symmetric attachment, (2) asymmetric preferential attachment of new nodes with different in- and out-degrees, and (3) preferential attachment between pre-existing nodes (without addition of new nodes). In these three cases, both relation between network topology (e.g., average path length and betweenness centralization) and sparse synchronization and contributions of individual dynamics to the sparse synchronization are discussed.
authors.affiliation: Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu 705-115, Korea.
authors.forename: Sang-Yoon
authors.initials: SY
authors.lastname: Kim
authors.affiliation: Institute for Computational Neuroscience and Department of Science Education, Daegu National University of Education, Daegu 705-115, Korea.
authors.forename: Woochang
authors.initials: W
authors.lastname: Lim
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001921:Brain / Q000502:physiology*; D007395:Interneurons / Q000502:physiology*; D008959:Models, Neurological; D009433:Neural Inhibition / Q000502:physiology*; D016571:Neural Networks, Computer; D010507:Periodicity; D051381:Rats; D013269:Stochastic Processes