Ultrastructure of Paneth cells in the intestine of various mammals

Paneth cells in the following species were observed under an electron microscope: human, rhesus monkey, hare, guinea pig, rat, nude rat, mouse, golden hamster, and insect feeder bat. Secretory granules containing homogeneous electron-dense materials were observed in the Paneth cells of humans, monkeys, hares, guinea pigs, and bats; mouse Paneth-cell granules were bipartite (central core and peripheral halo), and the Paneth cells in rats and golden hamsters had secretory granules showing various electron densities. In humans, monkeys, and bats, immature granules near the Golgi apparatus sometimes showed bipartite substructure. The number and size of secretory granules were also diverse among various animal species. Some lysosome-like bodies were commonly observed in peri- or supranuclear regions, though the size and shape of the bodies differed from cell to cell. In apical cytoplasm, small clear vesicles (100–200 nm diameter) were more-or-less observed in all species examined, and it was especially note that rat Paneth cells contained many clear vesicles. Small dense-cored vesicles (150–200 nm diameter) were rare. It is unlikely that the various ultrastructural features of Paneth cells correlate with the phylogenetical classification.     

Flexible manipulator inspired by octopus: development of soft arms using sponge and experiment for grasping various objects

In this study, we focus on the intelligent behavior of an octopus and describe the development of a flexible manipulator. To realize the intelligent behavior, we employ sponges, rubbers and wires instead of electrical computers. The manipulator is controlled by the dynamics of the body such as the flexibility of the sponges, resilience of the rubbers and constraint by the wires. To demonstrate the effectiveness of the proposed manipulator, we conducted experiment for grasping various objects. We confirmed that grasping behaviors similar to those of an octopus can be realized by the dynamics of the body without electrical computers.     

Survey of actual viewing conditions at home and appropriate luminance of LCD‐TV screens

Abstract— To understand actual viewing conditions at home is important for TV design. And the preferred luminance level of LCD TVs under actual viewing conditions is also important in order to obtain both good picture quality and low power consumption. The actual viewing conditions of households and the preferred luminance levels was investigated. In a field test of 83 households, the display luminance, screen illuminance, and viewing locations were measured on site. In laboratory experiments, young and elderly subjects adjusted the luminance of an LCD‐TV screen to their preferred levels under different screen illuminance levels, angular screen sizes, and average luminance levels (ALL) of the images. As a result, two equations, which represent the preferred luminance level of LCD‐TV screens corresponding to different viewing conditions for young and elderly subjects were obtained. When the ALL of the images was 25% and the screen illuminance and angular screen size were set at 100 lx and 20°, respectively, the preferred luminance was 1 60 cd/m² for the young subjects and 248 cd/m² for the elderly subjects. By using the setting of the preferred luminance of an LCD TV under actual viewing conditions, it is possible to conserve energy consumption.     

Development and field test of the articulated mobile robot T2 Snake-4 for plant disaster prevention

Abstract

In this work, we develop an articulated mobile robot that can move in narrow spaces, climb stairs, gather information, and operate valves for plant disaster prevention. The robot can adopt a tall position using a folding arm and gather information using sensors mounted on the arm. In addition, this paper presents a stair climbing method using a single backward wave. This method enables the robot to climb stairs that have a short tread. The developed robot system is tested in a field test at the World Robot Summit 2018, and the lessons learned in the field test are discussed.     

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.     

Modeling birdsong learning with a chaotic Elman network

Among passerines, Bengali finches are known to sing extremely complex courtship songs with three hierarchical structures: namely, the element, the chunk, and the syntax. In this work, we theoretically studied the mechanism of the song of Bengali finches in aides to provide a dynamic view of the development of birdsong learning. We first constructed a model of the Elman network with chaotic neurons that successfully learned the supervisor signal defined by a simple finite-state syntax. Second, we focused on the process of individual-specific increases in the complexity of song syntax. We propose a new learning algorithm to produce the intrinsic diversification of song syntax without a supervisor on the basis of the itinerant dynamics of chaotic neural networks and the Hebbian learning rule. The emergence of novel syntax modifying the acquired syntax is demonstrated. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

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.