Three synaptic components contributing to robust network synchronization

Robust synchronized activity is widely observed in real neural systems. However, a mechanism for robust synchronization that can be understood analytically, and has a clear physical meaning, remains elusive. This paper considers such a mechanism by formalizing three synaptic components contributing to robust synchronization in networks with heterogeneous external drive currents and conductance-based synapses. The first component arises from the assumption that the aggregate post-synaptic potential of a neuron decays more if it fires later within a spike volley. The second component results because neurons with smaller drives have reached a lower membrane potential at the time when the volley of inputs arrives than that reached by neurons with larger drives. The third component arises from the assumption that neurons firing later in the previous volley have had less time to integrate their drives than neurons firing earlier have had, again causing a lower membrane potential at the time when the volley of inputs arrives. Because of the voltage-dependent properties of synaptic currents, either of the last two components will cause smaller inhibitions for the later-firing neurons if the synapses are inhibitory. This smaller inhibition causes the later-firing neurons to fire earlier in the next cycle, thereby forcing them toward synchrony. With these three synaptic components, we discuss the relationship between the robustness of the synchrony and the parameters, search for the optimal parameter set for the robust network synchronization of a certain frequency band, and demonstrate the key role of the voltage-dependent properties of synaptic currents in robust or stable synchronization.

CD24 gene exon 2 dimorphism does not affect disease susceptibility in Japanese sarcoidosis patients

BACKGROUND

CD24 proteins are expressed on several inflammatory cells, and play an important role for the T-cell activation.

OBJECTIVES

The aim of this study is to investigate the relationship of a CD24 gene polymorphism to disease susceptibility or clinical findings including bronchoalveolar lavage (BAL) cell profiles in Japanese sarcoidosis patients.

METHODS

A previously reported functional single nucleotide polymorphism (SNP) of CD24 gene exon 2 was examined in 186 Japanese sarcoidosis patients and 146 sex and age-matched healthy controls using restriction fragment length polymorphism method. The distribution of genotypes was compared between the two groups. The association between genotypes or alleles and clinical features or BAL cell profiles was also examined.

RESULTS

There were no significant differences in the distribution of genotypes or allele frequencies between sarcoidosis and controls. There were also no significant differences in clinical features or BAL cell profiles among patients with different genotypes of CD24.

CONCLUSIONS

There was no relationship between a CD24 exon 2 SNP and disease susceptibility or clinical findings in Japanese sarcoidosis patients.

Feedback mechanism in network dynamics with preferential flow

We study complex systems or networks in which each node holds an internal dynamics and interacts with other nodes through some kinds of topologies. Collective behavior with dynamical fluctuations is analyzed in complex systems. The dynamical fluctuations of a node can be divided into two parts: one is the internal dynamical fluctuation of the node and the other is the external dynamical fluctuation caused by other nodes. Based on a theoretical analysis, a hidden feedback mechanism is identified in complex systems, which is illustrated in a macroeconomic network and in a city-population network. Furthermore, we study the effect of the topology of the networks on the feedback mechanism. The feedback mechanism is preserved for hub nodes in the networks with a scale-free topology as well as in the networks with an evolving topology. By the hidden feedback mechanism, the observation data can be utilized to judge directly whether the system of each node is with positive feedback or with negative feedback even without knowing its dynamical model.

Reconstruction of transcription-translation dynamics with a model of gene networks

A transcription-translation model of gene networks and a method to reconstruct it from gene expression data are proposed. The model is a hybrid system based on the Glass network with continuous-time dynamics and logical interactions. Transcription-translation dynamics is introduced into the Glass network. The reconstruction of gene networks is reduced to the problem of estimating logical functions from binary representations of quantities of mRNAs and proteins. The reconstruction method is applied to the gene expression data of circadian rhythms. The response characteristics of the reconstructed gene network to periodic stimuli are analyzed. The results suggest the existence of a receiver gene that responds to an external signal, consistently with biological knowledge.

Multicommunity weight-driven bipartite network model

Community structure and rewiring phenomena exist in many complex networks, particularly in bipartite networks. We construct a model for the degree distribution of the rewiring problem in a multicommunity weight-driven bipartite network (MCWBN). The network consists of many interconnected communities, each of which holds a bipartite graph. The bipartite graph consists of two sets of nodes. We name each node in one set a "producer" and each node in the other set a "consumer." A weight value matrix defining the trade barrier between any two communities is used to characterize the structure of the communities, which ensures the higher preferential attachment probability in intracommunity than in intercommunity. The size of one producer is defined as the number of consumers connected to it. We find that the nonlinear dynamics of the scale of production, or the total size of all producers in each community is dependent only on the initial scale of production in each community, and independent of the distribution of the producer size. Furthermore, if the nonlinear system of the scale of production in each community is at an equilibrium state, the distribution of the producer size in each community of the MCWBN model is equivalent to that in a one-community model.

Can a spinning egg really jump?

Simultaneous three-way observation of optical, acoustic and electric properties demonstrates a theoretical prediction that a spinning prolate spheroid can spontaneously lose contact with the table in the course of its rising motion when the contact friction is weak and the spin is large enough. The durations of the first loss of contact are measured for various initial spin rates and for three aspect ratios. The measurements show good agreements with numerical simulations. It is also visually shown that a spinning hard-boiled egg can jump.

Crisis-induced intermittency in two coupled chaotic maps: towards understanding chaotic itinerancy

The present paper considers crisis-induced intermittency in a system composed of two coupled logistic maps. Its purpose is to clarify a bifurcation scenario generating such intermittent behaviors that can be regarded as a simple example of chaotic itinerancy. The intermittent dynamics appears immediately after an attractor-merging crisis of two off-diagonal chaotic attractors in a symmetrically coupled system. The scenario for the crisis is investigated through analyses of sequential bifurcations leading to the two chaotic attractors and successive changes in basin structures with variation of a system parameter. The successive changes of the basins are also characterized by variation of a dimension of a fractal basin boundary. A numerical analysis shows that simultaneous contacts between the attractors and the fractal basin boundary bring about the crisis and a snap-back repeller generated at the crisis produces the intermittent transitions. Furthermore, a modified scenario for intermittent behaviors in an asymmetrically coupled system is also discussed.

Surrogate analysis for detecting nonlinear dynamics in normal vowels

Normal vowels are known to have irregularities in the pitch-to-pitch variation which is quite important for speech signals to be perceived as natural human sound. Such pitch-to-pitch variation of vowels is studied in the light of nonlinear dynamics. For the analysis, five normal vowels recorded from three male and two female subjects are exploited, where the vowel signals are shown to have normal levels of the pitch-to-pitch variation. First, by the false nearest-neighbor analysis, nonlinear dynamics of the vowels are shown to be well analyzed by using a relatively low-dimensional reconstructing dimension of 4 < or = d < or = 7. Then, we further studied nonlinear dynamics of the vowels by spike-and-wave surrogate analysis. The results imply that there exists nonlinear dynamical correlation between one pitch-waveform pattern to another in the vowel signals. On the basis of the analysis results, applicability of the nonlinear prediction technique to vowel synthesis is discussed.