Development of a current detection type cable fault locator

A cable fault location instrument based on the detection of traveling wave currents was developed. It is different from the conventional pulse radar method based on voltage detection. Measurement is automated, and the distance to the fault point is displayed by merely pushing a switch. In a branched or crossbonded line, the fault point can be located without interference from the branched or the crossbonded point. Tests performed in both real and simulated lines confirmed the practical utility of this instrument     

Dynamical coherence patterns in neural field model at criticality

Phase synchronization is a mechanism that plays a crucial role in information processing in the brain, and coherence is one of the factors used to evaluate the pairwise degree of phase synchronization. Coherence is also an important measure for examining brain functions because it implies communication and cooperation among neurons. In this work, we study the coherence patterns of spontaneous activity in a neural field model at criticality where a second-order phase transition occurs with special properties that differentiate it from other regions. The results are summarized as follows. First, in high-frequency bands, the system outside the critical region is unable to communicate efficiently via phase synchronization. Second, the dynamical coherence patterns at the criticality show switching between high and low coherence states. Finally, we found that in a very brief period, there is high broadband coherence between some pairs of spatial points. This phenomenon can be observed only in the critical region.     

Integrative Bayesian model on two opposite types of sensory adaptation

Adaptation is a fundamental property of human perception. Recently, it was found that there are two opposite types of adaptation to repetitive stimuli with a temporal difference. In this article, we construct an integrative model of adaptation. We model the perception as a Bayesian inference, and represent the two types of adaptation as changes in the likelihood function and the prior distribution in the Bayesian inference. We examine our model analytically and show how the types of adaptation depend on model parameters.     

Greedy versus social: resource-competing oscillator network as a model of amoeba-based neurocomputer

A single-celled amoeboid organism, the true slime mold Physarum polycephalum, exhibits rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. The authors previously created a biocomputer that incorporates the organism as a computing substrate to search for solutions to combinatorial optimization problems. With the assistance of optical feedback to implement a recurrent neural network model, the organism changes its shape by alternately growing and withdrawing its photosensitive branches so that its body area can be maximized and the risk of being illuminated can be minimized. In this way, the organism succeeded in finding the optimal solution to the four-city traveling salesman problem with a high probability. However, it remains unclear how the organism collects, stores, and compares information on light stimuli using the oscillatory dynamics. To study these points, we formulate an ordinary differential equation model of the amoeba-based neurocomputer, considering the organism as a network of oscillators that compete for a fixed amount of intracellular resource. The model, called the “Resource-Competing Oscillator Network (RCON) model,” reproduces well the organism’s experimentally observed behavior, as it generates a number of spatiotemporal oscillation modes by keeping the total sum of the resource constant. Designing the feedback rule properly, the RCON model comes to face a problem of optimizing the allocation of the resource to its nodes. In the problem-solving process, “greedy” nodes having the highest competitiveness are supposed to take more resource out of other nodes. However, the resource allocation pattern attained by the greedy nodes cannot always achieve a “socially optimal” state in terms of the public cost. We prepare four test problems including a tricky one in which the greedy pattern becomes “socially unfavorable” and investigate how the RCON model copes with these problems. Comparing problem-solving performances of the oscillation modes, we show that there exist some modes often attain socially favorable patterns without being trapped in the greedy one.     

Amoeba-based Chaotic Neurocomputing: Combinatorial Optimization by Coupled Biological Oscillators

We demonstrate a neurocomputing system incorporating an amoeboid unicellular organism, the true slime mold Physarum, known to exhibit rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. Introducing optical feedback applied according to a recurrent neural network model, we induce that the amoeba’s photosensitive branches grow or degenerate in a network-patterned chamber in search of an optimal solution to the traveling salesman problem (TSP), where the solution corresponds to the amoeba’s stably relaxed configuration (shape), in which its body area is maximized while the risk of being illuminated is minimized.Our system is capable of reaching the optimal solution of the four-city TSP with a high probability. Moreover, our system can find more than one solution, because the amoeba can coordinate its branches’ oscillatory movements to perform transitional behavior among multiple stable configurations by spontaneously switching between the stabilizing and destabilizing modes. We show that the optimization capability is attributable to the amoeba’s fluctuating oscillatory movements. Applying several surrogate data analyses, we present results suggesting that the amoeba can be characterized as a set of coupled chaotic oscillators.

Networked reinforcement learning

Recently, many models of reinforcement learning with hierarchical or modular structures have been proposed. They decompose a task into simpler subtasks and solve them by using multiple agents. However, these models impose certain restrictions on the topological relations of agents and so on. By relaxing these restrictions, we propose networked reinforcement learning, where each agent in a network acts autonomously by regarding the other agents as a part of its environment. Although convergence to an optimal policy is no longer assured, by means of numerical simulations, we show that our model functions appropriately, at least in certain simple situations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Bayesian inference explains perception of unity and ventriloquism aftereffect: identification of common sources of audiovisual stimuli

We study a computational model of audiovisual integration by setting a Bayesian observer that localizes visual and auditory stimuli without presuming the binding of audiovisual information. The observer adopts the maximum a posteriori approach to estimate the physically delivered position or timing of presented stimuli, simultaneously judging whether they are from the same source or not. Several experimental results on the perception of spatial unity and the ventriloquism effect can be explained comprehensively if the subjects in the experiments are regarded as Bayesian observers who try to accurately locate the stimulus. Moreover, by adaptively changing the inner representation of the Bayesian observer in terms of experience, we show that our model reproduces perceived spatial frame shifts due to the audiovisual adaptation known as the ventriloquism aftereffect.     

Selectivity and stability via dendritic nonlinearity

Inspired by recent studies regarding dendritic computation, we constructed a recurrent neural network model incorporating dendritic lateral inhibition. Our model consists of an input layer and a neuron layer that includes excitatory cells and an inhibitory cell; this inhibitory cell is activated by the pooled activities of all the excitatory cells, and it in turn inhibits each dendritic branch of the excitatory cells that receive excitations from the input layer. Dendritic nonlinear operation consisting of branch-specifically rectified inhibition and saturation is described by imposing nonlinear transfer functions before summation over the branches. In this model with sufficiently strong recurrent excitation, on transiently presenting a stimulus that has a high correlation with feed- forward connections of one of the excitatory cells, the corresponding cell becomes highly active, and the activity is sustained after the stimulus is turned off, whereas all the other excitatory cells continue to have low activities. But on transiently presenting a stimulus that does not have high correlations with feedforward connections of any of the excitatory cells, all the excitatory cells continue to have low activities. Interestingly, such stimulus-selective sustained response is preserved for a wide range of stimulus intensity. We derive an analytical formulation of the model in the limit where individual excitatory cells have an infinite number of dendritic branches and prove the existence of an equilibrium point corresponding to such a balanced low-level activity state as observed in the simulations, whose stability depends solely on the signal-to-noise ratio of the stimulus. We propose this model as a model of stimulus selectivity equipped with self-sustainability and intensity-invariance simultaneously, which was difficult in the conventional competitive neural networks with a similar degree of complexity in their network architecture. We discuss the biological relevance of the model in a general framework of computational neuroscience.     

Consistency property of extended least-squares parameter estimation for stochastic diffusion equation

The extended least-squares parameter estimate for stochastic heat diffusion equations is considered. The unknown parameter is a heat diffusion coefficient which is a function of a spatial variable. Almost sure convergence for the estimated parameter is proved. A numerical example is demonstrated for supporting the theoretical results developed here.     

TEM/STEM Observation of ZrC Coating Layer for Advanced High-Temperature Gas-Cooled Reactor Fuel, Part II

The Japan Atomic Energy Agency (JAEA) has started to study and develop zirconium carbide (ZrC)-coated fuel particles for advanced high-temperature gas-cooled reactors. The ZrC coating layer has been fabricated at JAEA by chemical vapor deposition using a pyrolytic reaction of zirconium bromide. The microstructures of the ZrC layers, whose nominal deposition temperatures could be measured and controlled during the deposition process, were characterized by means of TEM and STEM. In the present study, three batches were prepared and compared with each other as well as the previous batches. The crystallographic orientation of ZrC with regard to the growth direction in the ZrC layers deposited at a constant temperature of 1630 K was different from that deposited at varying temperatures in the 1493–1823 K range. A thin layer of turbostratic carbon was observed at the boundary between pyrolytic carbon and ZrC in particles deposited at the highest temperature among those used in this study (the nominal temperature was 1769 K); no such structure was found in a batch deposited at a lower temperature (the nominal temperature was 1632 K). Therefore, precise control of temperature is shown to be critical to the formation of good ZrC coatings.