Variability v.s. synchronicity of neuronal activity in local cortical network models with different wiring topologies.
Dynamical behavior of a biological neuronal network depends significantly on the spatial pattern of synaptic connections among neurons. While neuronal network dynamics has extensively been studied with simple wiring patterns, such as all-to-all or random synaptic connections, not much is known about the activity of networks with more complicated wiring topologies. Here, we examined how different wiring topologies may influence the response properties of neuronal networks, paying attention to irregular spike firing, which is known as a characteristic of in vivo cortical neurons, and spike synchronicity. We constructed a recurrent network model of realistic neurons and systematically rewired the recurrent synapses to change the network topology, from a localized regular and a "small-world" network topology to a distributed random network topology. Regular and small-world wiring patterns greatly increased the irregularity or the coefficient of variation (Cv) of output spike trains, whereas such an increase was small in random connectivity patterns. For given strength of recurrent synapses, the firing irregularity exhibited monotonous decreases from the regular to the random network topology. By contrast, the spike coherence between an arbitrary neuron pair exhibited a non-monotonous dependence on the topological wiring pattern. More precisely, the wiring pattern to maximize the spike coherence varied with the strength of recurrent synapses. In a certain range of the synaptic strength, the spike coherence was maximal in the small-world network topology, and the long-range connections introduced in this wiring changed the dependence of spike synchrony on the synaptic strength moderately. However, the effects of this network topology were not really special in other properties of network activity.
Kitano
Katsunori
K
Department of Human and Computer Intelligence, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan. kitano@ci.ritsumei.ac.jp
Fukai
Tomoki
T
D000200:Action Potentials / Q000187:drug effects / Q000502:physiology*; D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology*; D018690:Excitatory Amino Acid Agonists / Q000494:pharmacology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000187:drug effects / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000187:drug effects / Q000502:physiology*; D013569:Synapses / Q000187:drug effects / Q000502:physiology; D013997:Time Factors; D018350:alpha-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid / Q000494:pharmacology
D018690:Excitatory Amino Acid Agonists; D018350:alpha-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid