Recurrent infomax generates cell assemblies, neuronal avalanches, and simple cell-like selectivity

Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly-like and synfire chain-like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.     

Design of an adaptive fuzzy sliding mode control for uncertain discrete-time nonlinear systems based on noisy measurements

This paper presents the design of an adaptive fuzzy sliding mode control (AFSMC) for uncertain discrete-time nonlinear dynamic systems. The dynamic systems are described by a discrete-time state equation with nonlinear uncertainties, and the uncertainties include the modelling errors and the external disturbances to be unknown but nonlinear with the bounded properties. The states are measured by the restriction of measurement sensors and the contamination with independent measurement noises. The nonlinear uncertainties are approximated by using the fuzzy IF-THEN rules based on the universal approximation theorem, and the approximation error is compensated by adding an adaptive complementary term to the proposed AFSMC. The fuzzy inference approach based on the extended single input rule modules is proposed to reduce the number of the fuzzy IF-THEN rules. The estimates for the un-measurable states and the adjustable parameters are obtained by using the weighted least squares estimator and its simplified one. It is proved that under some conditions the estimation errors will remain in the vicinity of zero as time increases, and the states are ultimately bounded subject to the proposed AFSMC. The effectiveness of the proposed method is indicated through the simulation experiment of a simple numerical system.     

Self-organizing maps with asymmetric neighborhood function

The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space to a low-dimensional representation space. It has recently found wide applications in such areas as visualization, classification, and mining of various data. However, when the data sets to be processed are very large, a copious amount of time is often required to train the map, which seems to restrict the range of putative applications. One of the major culprits for this slow ordering time is that a kind of topological defect (e.g., a kink in one dimension or a twist in two dimensions) gets created in the map during training. Once such a defect appears in the map during training, the ordered map cannot be obtained until the defect is eliminated, for which the number of iterations required is typically several times larger than in the absence of the defect. In order to overcome this weakness, we propose that an asymmetric neighborhood function be used for the SOM algorithm. Compared with the commonly used symmetric neighborhood function, we found that an asymmetric neighborhood function accelerates the ordering process of the SOM algorithm, though this asymmetry tends to distort the generated ordered map. We demonstrate that the distortion of the map can be suppressed by improving the asymmetric neighborhood function SOM algorithm. The number of learning steps required for perfect ordering in the case of the one-dimensional SOM is numerically shown to be reduced from O(N(3)) to O(N(2)) with an asymmetric neighborhood function, even when the improved algorithm is used to get the final map without distortion.     

Basic constructs of the SIM operating system

The basic constructs of SIMPOS (Sequential Inference Machine Programming and Operating System) are explained. SIMPOS is an operating system for a super-personal computer (SIM), based on logic programming language (a modified Prolog, called KL0). Our design principle is simplicity both in concept and structure. The entire system will be constructed using these basic constructs.     

Theory of ion mobility in liquid3He

An ion interacting with quasiparticles in liquid³He is treated theoretically by summing most divergent terms in perturbation series of the self-energy of the ion and the vertex part. The ion Green's function is renormalized by a factorZ(T), and the vertex part byZ(T) ^?1, where \({\text{Z(T)}} = {\text{(T/T}}_F {\text{)}}^{{\text{2V}}_{{\text{0}}^{\rho ^{\text{2}} } }^{\text{2}} } \) , forT ₀?T?T _F. Here,T ₀=(m/M)T _F, withm the³He mass andM the ion mass, and \({\text{V}}_{0^\rho } \) is the strength of the interaction. The factor explains the weak temperature dependence of the mobility around the minimum atT ₀; we also discuss its effect on the behavior of the mobility in³He-B nearT _c.     

Theoretical and evolutionary parameter tuning of neural oscillators with a double-chain structure for generating rhythmic signals

A neural oscillator with a double-chain structure is one of the central pattern generator models used to simulate and understand rhythmic movements in living organisms. However, it is difficult to reproduce desired rhythmic signals by tuning an enormous number of parameters of neural oscillators. In this study, we propose an automatic tuning method consisting of two parts. The first involves tuning rules for both the time constants and the amplitude of the oscillatory outputs based on theoretical analyses of the relationship between parameters and outputs of the neural oscillators. The second involves an evolutionary tuning method with a two-step genetic algorithm (GA), consisting of a global GA and a local GA, for tuning parameters such as neural connection weights that have no exact tuning rule. Using numerical experiments, we confirmed that the proposed tuning method could successfully tune all parameters and generate sinusoidal waves. The tuning performance of the proposed method was less affected by factors such as the number of excitatory oscillators or the desired outputs. Furthermore, the proposed method was applied to the parameter-tuning problem of some types of artificial and biological wave reproduction and yielded optimal parameter values that generated complex rhythmic signals in Caenorhabditis elegans without trial and error.     

A high‐performance sorting algorithm for multicore single‐instruction multiple‐data processors

Many sorting algorithms have been studied in the past, but there are only a few algorithms that can effectively exploit both single‐instruction multiple‐data (SIMD) instructions and thread‐level parallelism. In this paper, we propose a new high‐performance sorting algorithm, called aligned‐access sort (AA‐sort), that exploits both the SIMD instructions and thread‐level parallelism available on today's multicore processors. Our algorithm consists of two phases, an in‐core sorting phase and an out‐of‐core merging phase. The in‐core sorting phase uses our new sorting algorithm that extends combsort to exploit SIMD instructions. The out‐of‐core algorithm is based on mergesort with our novel vectorized merging algorithm. Both phases can take advantage of SIMD instructions. The key to high performance is eliminating unaligned memory accesses that would reduce the effectiveness of SIMD instructions in both phases. We implemented and evaluated the AA‐sort on PowerPC 970MP and Cell Broadband Engine platforms. In summary, a sequential version of the AA‐sort using SIMD instructions outperformed IBM's optimized sequential sorting library by 1.8 times and bitonic mergesort using SIMD instructions by 3.3 times on PowerPC 970MP when sorting 32 million random 32‐bit integers. Also, a parallel version of AA‐sort demonstrated better scalability with increasing numbers of cores than a parallel version of bitonic mergesort on both platforms. Copyright © 2011 John Wiley & Sons, Ltd.     

Breakdown Phenomena in Point-to-Point Gaps

Measurements with multiple techniques delineating the complete sequence of events from the primary streamer to the formation of the spark channel for relatively small point-to-point gaps are reported. The pulsed potential with 0.4 μs rise time and 1800 μs decay time was applied to the point-to-point gap, of which length was changed from 6 to 14 cm. The light pulses were observed by 5 photomultipliers as well as current and potential wave. It is shown that the spark channel can be materialized through the following three processes: 1) initial process, in which the primary, the secondary, and the tertiary streamers develop from both point electrodes into gap, and the ionizing wave bridges the gap; 2) intermediate process, in which the dark period proceeds with ion and electron movement if the applied potential is around the mean breakdown potential, or in the other case, the ``leader' develops from the anode with the help of ionizing waves; and 3) final process, in which the channel is highly ionized at first by the ionizing wave, then gas heating, to lead the formation of a spark channel. On the bases of the measurements of streamer speed and current, the ion and electron density were calculated. It is estimated that starting with the primary streamer with an electron density of 1012 cm-3, the electron density in the streamer channel is augmented by the ionizing wave up to 1012cm -3 when the gap is bridged by the ionizing wave.     

Theory of the excitonic fluctuation in a strong magnetic field. I. Development of the physical picture and a self-consistent Hartree theory

The physical picture of the appearance of the excitonic correlation in a strong magnetic field is developed in an electron-hole system which consists of one conduction band and one valence band. The field is assumed to be sufficiently strong that all the electrons and holes occupy their lowest Landau levels. Kinematical and dynamical aspects of such a truncated system are discussed in detail. The discussion does not deny the possibility of the excitonic phase transition in a strong magnetic field. The essence of the present physics is in the high degeneracy of the single-particle state and the strong shrinking of the electron wave function due to the magnetic field. In order to study the physics explicitly we construct a self-consistent Hartree theory. Further introducing static and local approximations for the excitonic fluctuation. We can determine the quasiparticle state and the fluctuation exactly near the transition point. We present the transition temperature in two limiting cases of the band configuration. We indicate the self-consistent Hartree theory combined with the static and local approximations is an unsatisfactory theory in the light of our physical picture. We also discuss possible improvements of the theory briefly.