Variability v.s. synchronicity of neuronal activity in local cortical network models with different wiring topologies

Abstract

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.

Experimental analysis and computational modeling of interburst intervals in spontaneous activity of cortical neuronal culture

“…The former is based upon the probability of quantal release, randomness of diffusion, and chemical reaction, or on the unpredictability of responses from ion channels within the synaptic cleft (Mainen and Sejnowski 1995;Stevens and Zador 1998;Rodriguez-Molina et al 2007). Vladimirski et al (2008) showed that noise-driven, unstructured networks require large, nonphysiological values of synaptic parameters in order to produce robust network bursts, while structured networks can use lower values for the same purpose (Kitano and Fukai 2007). To trigger spontaneous activity we simulated both network types, i.e., noise-and pacemaker-driven networks.…”

The Role of the BDNF Val66Met Polymorphism for the Synchronization of Error-Specific Neural Networks

“…Possibilities include largely distributed changes in structural (Cohen-Cory et al, 2010) and functional connectivity (Thomason et al, 2009). As connectivity determines neural network synchronization and dynamics (Lago-Fernández et al, 2001;Kitano and Fukai, 2007), synchronization may constitute a relevant mechanism by which BDNF affects cognitive functions. Such possible mechanisms have until now not been investigated.…”

Roles of Inhibitory Neurons in Rewiring-Induced Synchronization in Pulse-Coupled Neural Networks

“…Based on these findings, we analyzed the dynamics of networks composed of both excitatory neurons and inhibitory neurons. A similar network has already been analyzed by Kitano & Fukai (2007), but, to our knowledge, this is the first report to show that the network topology required for the generation of synchrony is determined mainly by the dynamics of the inhibitory neurons.…”

“…All the networks treated in these works show global synchronization at p = 1. On the other hand, in some networks, the dependence of p 0 on the values of the parameters is not so much clear (LagoFernández et al, 2000;Masuda & Aihara, 2004;Netoff et al, 2004;Roxin, Riecke, & Solla, 2004;Kitano & Fukai, 2007). It is a future work to understand the relationship between the above two groups of studies.…”

“…Kitano & Fukai (2007) examined the dynamics of networks composed of excitatory and inhibitory neurons, whose structures are obtained by rewiring the connections of a local network with the rewiring probability p. The values of the parameters of their network were chosen so that the neurons do not show synchronous oscillations at p = 1. The present paper investigates the dynamics of a similar network and examines the dependence of the synchronization on the network structure, but we set the parameters of the network to show synchronous oscillations at p = 1 because we wish to clarify whether the synchronous oscillations persist even for small p.…”