Detection of current-sheet and bipolar ion flows in a self-generated antiparallel magnetic field of laser-produced plasmas for magnetic reconnection research.

Magnetic reconnection in laser-produced magnetized plasma is investigated by using optical diagnostics. The magnetic field is generated via the Biermann battery effect, and the inversely directed magnetic field lines interact with each other. It is shown by self-emission measurement that two colliding plasmas stagnate on a midplane, forming two planar dense regions, and that they interact later in time. Laser Thomson scattering spectra are distorted in the direction of the self-generated magnetic field, indicating asymmetric ion velocity distribution and plasma acceleration. In addition, the spectra perpendicular to the magnetic field show different peak intensity, suggesting an electron current formation. These results are interpreted as magnetic field dissipation, reconnection, and outflow acceleration. Two-directional laser Thomson scattering is, as discussed here, a powerful tool for the investigation of microphysics in the reconnection region.

High-power laser experiment on developing supercritical shock propagating in homogeneously magnetized plasma of ambient gas origin.

A developing supercritical collisionless shock propagating in a homogeneously magnetized plasma of ambient gas origin having higher uniformity than the previous experiments is formed by using high-power laser experiment. The ambient plasma is not contaminated by the plasma produced in the early time after the laser shot. While the observed developing shock does not have stationary downstream structure, it possesses some characteristics of a magnetized supercritical shock, which are supported by a one-dimensional full particle-in-cell simulation taking the effect of finite time of laser-target interaction into account.

Direct observations of pure electron outflow in magnetic reconnection.

Magnetic reconnection is a universal process in space, astrophysical, and laboratory plasmas. It alters magnetic field topology and results in energy release to the plasma. Here we report the experimental results of a pure electron outflow in magnetic reconnection, which is not accompanied with ion flows. By controlling an applied magnetic field in a laser produced plasma, we have constructed an experiment that magnetizes the electrons but not the ions. This allows us to isolate the electron dynamics from the ions. Collective Thomson scattering measurements reveal the electron Alfvénic outflow without ion outflow. The resultant plasmoid and whistler waves are observed with the magnetic induction probe measurements. We observe the unique features of electron-scale magnetic reconnection simultaneously in laser produced plasmas, including global structures, local plasma parameters, magnetic field, and waves.

High-power laser experiment forming a supercritical collisionless shock in a magnetized uniform plasma at rest.

We present an experimental method to generate quasiperpendicular supercritical magnetized collisionless shocks. In our experiment, ambient nitrogen (N) plasma is at rest and well magnetized, and it has uniform mass density. The plasma is pushed by laser-driven ablation aluminum (Al) plasma. Streaked optical pyrometry and spatially resolved laser collective Thomson scattering clarify structures of plasma density and temperatures, which are compared with one-dimensional particle-in-cell simulations. It is indicated that just after the laser irradiation, the Al plasma is magnetized by a self-generated Biermann battery field, and the plasma slaps the incident N plasma. The compressed external field in the N plasma reflects N ions, leading to counterstreaming magnetized N flows. Namely, we identify the edge of the reflected N ions. Such interacting plasmas form a magnetized collisionless shock.

Reconstructing bifurcation diagrams only from time-series data generated by electronic circuits in discrete-time dynamical systems.

Bifurcation-diagram reconstruction estimates various attractors of a system without observing all of them but only from observing several attractors with different parameter values. Therefore, the bifurcation-diagram reconstruction can be used to investigate how attractors change with the parameter values, especially for real-world engineering and physical systems for which only a limited number of attractors can be observed. Although bifurcation diagrams of various systems have been reconstructed from time-series data generated in numerical experiments, the systems that have been targeted for reconstructing bifurcation diagrams from time series measured from physical phenomena so far have only been continuous-time dynamical systems. In this paper, we reconstruct bifurcation diagrams only from time-series data generated by electronic circuits in discrete-time dynamical systems with different parameter values. The generated time-series datasets are perturbed by dynamical noise and contaminated by observational noise. To reconstruct the bifurcation diagrams only from the time-series datasets, we use an extreme learning machine as a time-series predictor because it has a good generalization property. Hereby, we expect that the bifurcation-diagram reconstruction with the extreme learning machine is robust against dynamical noise and observational noise. For quantitatively verifying the robustness, the Lyapunov exponents of the reconstructed bifurcation diagrams are compared with those of the bifurcation diagrams generated in numerical experiments and by the electronic circuits.

Effect of mixed coupling on relay-coupled Rössler and Lorenz oscillators.

The complete synchronization between the outermost oscillators using the mixed coupling in relay coupled systems is studied. Mixed coupling has two types of coupling functions: coupling between similar or dissimilar variables. We examine the complete synchronization in relay-coupled systems by the largest transverse Lyapunov exponent and synchronization error. We show numerically for Rössler and Lorenz oscillators that the combination of these two types of coupling functions is able to decrease the critical coupling strength for complete synchronization as well as it also suppress oscillations for larger coupling strength.

Dynamical correlation patterns and corresponding community structure in neural spontaneous activity at criticality.

It has been considered that the state in the vicinity of a critical point, which is the point between ordered and disordered states, can underlie and facilitate information processing of the brain in various aspects. In this research, we numerically study the influence of criticality on one aspect of brain information processing, i.e., the community structure, which is an important characteristic of complex networks. We examine community structure of the functional connectivity in simulated brain spontaneous activity, which is based on dynamical correlations between neural activity patterns at different positions. The brain spontaneous activity is simulated by a neural field model whose parameter covers subcritical, critical, and supercritical regions. Then, the corresponding dynamical correlation patterns and community structure are compared. In the critical region, we found some distinctive properties, namely high correlation and correlation switching, high modularity and a low number of modules, high stability of the dynamical functional connectivity, and moderate flexibility of the community structure across temporal scales. We also discuss how these characteristics might improve information processing of the brain.

Tonotopic-column-dependent variability of neural encoding in the auditory cortex of rats.

Neural computation could benefit from the heterogeneity of neurons to achieve energy efficiency. Beyond a single neuron level, adaptation to biologically important signals should also make functional columns heterogeneous. In the present study, we test a hypothesis that variability of neural response depends on tonotopic columns in the primary auditory cortex (A1) of rats. Mutual information (MI) was estimated from multi-unit responses in A1 of anesthetized rats, to quantify how spike count (SC) and the first spike latency (FSL) carried information about frequency and intensity of test tones. Consequently, for both SC and FSL, we found best frequency (BF)-dependent MI distributions with wide variances in high BF regions. These MI distributions were caused by BF-dependence of the amount of information that neurons conveyed, i.e., total entropy, rather than the transmission efficiency. In addition, the relationship between the transmission efficiency and the total entropy differentiated SC encoding and FSL encoding, suggesting that SC encoding and FSL encoding are not redundant but each plays a different role in intensity encoding. These results provide compelling evidence that BF columns are heterogeneous. Such heterogeneity of columns may make the global computation in A1 more efficient. Thus, the efficient coding in the neural system could be achieved by multiple-scale heterogeneity.

A CD40 single-nucleotide polymorphism affects the lymphocyte profiles in the bronchoalveolar lavage of Japanese patients with sarcoidosis.

CD40 plays a critical role in adaptive immunity, and alveolar macrophages in patients with sarcoidosis express higher levels of CD40. This study investigated the association of rs1883832, a functional single-nucleotide polymorphism in the CD40 gene with susceptibility to sarcoidosis and phenotypes of sarcoidosis. Genotyping of rs1883832 in 175 Japanese patients with sarcoidosis and 150 age- and sex-matched controls revealed no significant difference between the genotypes of the patient and control groups (CC/CT/TT, 32.8/52.0/14.7% in the patients; 37.3/48.0/14.7% in the controls, P = 0.66; allele C, 59.1% in the patients, 61.3% in the controls, P = 0.57). T-cell and CD4+ cell counts in the bronchoalveolar lavage fluid were significantly higher in the TT genotype group than in the CC and CT genotype group.

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.

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.

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.

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.

Neuron-synapse IC chip-set for large-scale chaotic neural networks.

We propose a neuron-synapse integrated circuit (IC) chip-set for large-scale chaotic neural networks. We use switched-capacitor (SC) circuit techniques to implement a three-internal-state transiently-chaotic neural network model. The SC chaotic neuron chip faithfully reproduces complex chaotic dynamics in real numbers through continuous state variables of the analog circuitry. We can digitally control most of the model parameters by means of programmable capacitive arrays embedded in the SC chaotic neuron chip. Since the output of the neuron is transfered into a digital pulse according to the all-or-nothing property of an axon, we design a synapse chip with digital circuits. We propose a memory-based synapse circuit architecture to achieve a rapid calculation of a vast number of weighted summations. Both of the SC neuron and the digital synapse circuits have been fabricated as IC forms. We have tested these IC chips extensively, and confirmed the functions and performance of the chip-set. The proposed neuron-synapse IC chip-set makes it possible to construct a scalable and reconfigurable large-scale chaotic neural network with 10000 neurons and 10000/sup 2/ synaptic connections.

Synchronization of pulse-coupled excitable neurons.

Collective behavior of pulse-coupled oscillatory neurons has been investigated widely. In many cases, however, real neurons are intrinsically not oscillatory but excitable. The networks of excitable neurons can have their own characteristic dynamics, and they are of interest also from the viewpoint of functional assemblies. In the present paper, the collective behavior of pulse-coupled excitable neurons has been investigated using phase description. It is shown that full synchronization is achieved in networks of excitable leaky integrate-and-fire neurons and discrete-time Nagumo-Sato neurons. The cooperative roles of external spike inputs, decay of internal states, and feedback spikes are explained. Enhancement of synchronization by refractoriness and noise is also reported.