Extrasynaptic-GABA-mediated neuromodulation in a sensory cortical neural network

Simulating a neural network model of an early sensory cortical area, we investigated how gamma-aminobutyric acid (GABA) accumulated in extracellular space (ambient GABA), which depends on the synaptic activity of GABAergic interneurons, acts on the GABAa-receptors located on extrasynaptic membrane regions of principal cells (P), feedback inhibitory cells (F) and lateral inhibitory cells (L). The ambient GABA enhanced the selective responsiveness of P-cells to a target feature stimulus, if it acted on the extrasynaptic GABAa-receptors of P-cells. The ambient GABA led to depolarizing P-cells during ongoing (spontaneous) neuronal-activity periods, if it acted on the extrasynaptic GABAa-receptors of F or L cells. This membrane depolarization contributed to establishing an ongoing subthreshold neuronal state, by which the P-cells could respond quickly to the target stimulus. We suggest that the combinatorial inhibition of P, F, and L cells, meditated by extrasynaptic GABAa-receptors recognizing ambient GABA, is crucial for processing the information of relevant sensory features and for establishing an ongoing subthreshold cortical state that prepares as a ready state for subsequent sensory input. A failure in neuronal-activity-dependent regulation of ambient GABA, stemming largely from the depletion of GABA in extracellular space during senescence, may cause the degeneration of intracortical inhibition that leads to cognitive dysfunction in old animals.     

A full‐color eyewear display using planar waveguides with reflection volume holograms

Abstract— A full‐color eyewear display with over 85% see‐through transmittance with a 16° horizontal field of view was developed. Very low color crosstalk, less than 0.008 Δu′v′ uniformity, and 120% NTSC color gamut were achieved. Waveguides with two in‐ and out‐coupling reflection volume hologram elements enabled a simple configuration that has an optical engine beside the user's temples. The reflection volume hologram elements used on the waveguides realized a small thickness of 1.4 mm for each waveguide, and an out‐coupling reflection volume hologram used as an optical combiner contributed a high see‐through transmittance of 85% due to its wavelength selectivity. However, there are technical challenges in achieving a reasonable screen size and quality color images with optics that utilize holographic waveguides because holograms have large chromatic dispersions compared to conventional optical elements such as lenses and mirrors. Approaches to overcome these issues are described.     

On-chip Diamond MEMS Magnetic Sensing through Multifunctionalized Magnetostrictive Thin Film

Electrically integrable, high-sensitivity, and high-reliability magnetic sensors are not yet realized at high temperatures (500 °C). In this study, an integrated on-chip single-crystal diamond (SCD) micro-electromechanical system (MEMS) magnetic transducer is demonstrated by coupling SCD with a large magnetostrictive FeGa film. The FeGa film is multifunctionalized to actuate the resonator, self-sense the external magnetic field, and electrically readout the resonance signal. The on-chip SCD MEMS transducer shows a high sensitivity of 3.2 Hz mT⁻¹ from room temperature to 500 °C and a low noise level of 9.45 nT Hz^−1/2 up to 300 °C. The minimum fluctuation of the resonance frequency is 1.9 × 10⁻⁶ at room temperature and 2.3 × 10⁻⁶ at 300 °C. An SCD MEMS resonator array with parallel electric readout is subsequently achieved, thus providing a basis for the development of magnetic image sensors. The present study facilitates the development of highly integrated on-chip MEMS resonator transducers with high performance and high thermal stability.     

Market prediction using machine learning based on social media specific features

Artificial Life and Robotics - In recent years, unspecified messages posted on social media have significantly affected the price fluctuations of online-traded products, such as stocks and virtual...     

Bounded Rationality,Group Formation and the Emergence of Trust: An Agent-Based Economic Model

Computational Economics - It is possible to model trust as an investment game, where a player in order to receive a reward or a better outcome, accepts a certain risk of defection by another...     

Revolving Vernier Mechanism Controls Size of Linear Homomultimer

A new kind of the Vernier mechanism that is able to control the size of linear assembly of DNA origami nanostructures is proposed. The mechanism is realized by mechanical design of DNA origami, which consists of a hollow cylinder and a rotatable shaft in it connected through the same scaffold. This nanostructure stacks with each other by the shape complementarity at its top and bottom surfaces of the cylinder, while the number of stacking is limited by twisting angle of the shaft. Experiments have shown that the size distribution of multimeric assembly of the origami depends on the twisting angle of the shaft; the average lengths of the multimer are decamer, hexamer, and tetramer for 0°, 10°, and 20° twist, respectively. In summary, it is possible to affect the number of polymerization by adjusting the precise shape and movability of a molecular structure.     

A biped static balance control and torque pattern learning under unknown periodic external forces

This paper addresses a biped balancing task in which an unknown external force is exerted, using the so-called ‘ankle strategy’ model. When an external force is periodic, a human adaptively maintains the balance, next learns how much force should be produced at the ankle joint from its repeatability, and finally memorized it as a motion pattern. To acquire motion patterns with balancing, we propose a control and learning method: as the control method, we adopt ground reaction force feedback to cope with an uncertain external force, while, as the learning method, we introduce a motion pattern generator that memorizes the torque pattern of the ankle joint by use of Fourier series expansion. In this learning process, the period estimation of the external force is crucial; this estimation is achieved based on local autocorrelation of joint trajectories. Computer simulations and robot experiments show effective control and learning results with respect to unknown periodic external forces.     

Incremental learning of gestures for human–robot interaction

For a robot to cohabit with people, it should be able to learn people’s nonverbal social behavior from experience. In this paper, we propose a novel machine learning method for recognizing gestures used in interaction and communication. Our method enables robots to learn gestures incrementally during human–robot interaction in an unsupervised manner. It allows the user to leave the number and types of gestures undefined prior to the learning. The proposed method (HB-SOINN) is based on a self-organizing incremental neural network and the hidden Markov model. We have added an interactive learning mechanism to HB-SOINN to prevent a single cluster from running into a failure as a result of polysemy of being assigned more than one meaning. For example, a sentence: “Keep on going left slowly” has three meanings such as, “Keep on (1)”, “going left (2)”, “slowly (3)”. We experimentally tested the clustering performance of the proposed method against data obtained from measuring gestures using a motion capture device. The results show that the classification performance of HB-SOINN exceeds that of conventional clustering approaches. In addition, we have found that the interactive learning function improves the learning performance of HB-SOINN.     

A study on a quantum-inspired evolutionary algorithm based on pair swap

A quantum-inspired evolutionary algorithm (QEA) is proposed as a stochastic algorithm to perform combinatorial optimization problems. The QEA is evolutionary computation that uses quantum bits and superposition states in quantum computation. Although the QEA is a coarse-grained parallel algorithm, it involves many parameters that must be adjusted manually. This paper proposes a new method, named pair swap, which exchanges each best solution information between two individuals instead of migration in the QEA. Experimental results show that our proposed method is a simpler algorithm and can find a high quality solution in the 0-1 knapsack problem. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007