Metabolic Changes during Defense Responses against Wound Stresses in Citrus Plants

Citrus plants are well known as a rich source of functional chemicals; however, metabolites involved in defense responses against environmental stresses are not yet well understood. Among environmental stresses, mechanical wounding is a continuous threat toward the growth and survival of plants. Recent advances in analytical technology and informatics enable comprehensive analysis of primary and secondary metabolites. In this chapter, metabolic profiling of leaf metabolites in seven Citrus species during responses against wound stress as well as defense-related phytohormone treatments was described. Moreover, we discussed current metabolomic techniques, application of these techniques to researches on Citrus defense responses and metabolic profiling-oriented identification of novel compounds

Burst synchronization in two pulse-coupled resonate-and-fire neuron circuits

The present paper addresses burst synchronization in out of phase observed in two pulse-coupled resonate-and-fire neuron (RFN) circuits. The RFN circuit is a silicon spiking neuron that has second-order membrane dynamics and exhibits fast subthreshold oscillation of membrane potential. Due to such dynamics, the behavior of the RFN circuit is sensitive to the timing of stimuli. We investigated the effects of the sensitivity and the mutual interaction on the dynamic behavior of two pulse-coupled RFN circuits, and will demonstrate out of phase burst synchronization and bifurcation phenomena through circuit simulations.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Fault-Tolerant Logic Gates Using Neuromorphic CMOS Circuits

Fault-tolerant design methods for VLSI circuits, which have traditionally been addressed at system level, will not be adequate for future very-deep submicron CMOS devices where serious degradation of reliability is expected. Therefore, a new design approach has been considered at low level of abstraction in order to implement robustness and faulttolerance into these devices. Moreover, fault tolerant properties of multi- layer feed-forward artificial neural networks have been demonstrated. Thus, we have implemented this concept at circuit-level, using spiking neurons. Using this approach, the NOT, NAND and NOR Boolean gates have been developed in the AMS 0.35 µm CMOS technology. A very straightforward mapping between the value of a neural weight and one physical parameter of the circuit has also been achieved. Furthermore, the logic gates have been simulated using SPICE corners analysis which emulates manufacturing variations which may cause circuit faults. Using this approach, it can be shown that fault-absorbing neural networks that operate as the desired function can be built

Active Brownian Motion in Threshold Distribution of a Coulomb Blockade Model

Randomly-distributed offset charges affect the nonlinear current-voltage property via the fluctuation of the threshold voltage of Coulomb blockade arrays. We analytically derive the distribution of the threshold voltage for a model of one-dimensional locally-coupled Coulomb blockade arrays, and propose a general relationship between conductance and the distribution. In addition, we show the distribution for a long array is equivalent to the distribution of the number of upward steps for aligned objects of different height. The distribution satisfies a novel Fokker-Planck equation corresponding to active Brownian motion. The feature of the distribution is clarified by comparing it with the Wigner and Ornstein-Uhlenbeck processes. It is not restricted to the Coulomb blockade model, but instructive in statistical physics generally.Comment: 4pages, 3figure

A Novel Analog CMOS Cellular Neural Network for Biologically-Inspired Walking Robot

Abstract-We propose a novel analog CMOS circuit that implements a class of cellular neural networks (CNNs) for biologically-inspired walking robots. Recently, a class of autonomous CNNs, so-called a reaction-diffusion (RD) CNN, has applied to locomotion control in robotics. We have introduced a novel RD-CNN, and implemented it as an analog CMOS circuit by using multiple-input floating-gate (MIFG) MOS FETs. As a result, the circuit can operate in voltage-mode. From the results on computer simulations, we have shown that the circuit has capability to generate stable rhythmic patterns for locomotion control in a quadruped walking robot

Burst synchronization in two pulse-coupled resonate-and-fire neuron circuits

The present paper addresses burst synchronization in out of phase observed in two pulse-coupled resonate-and-fire neuron (RFN) circuits. The RFN circuit is a silicon spiking neuron that has second-order membrane dynamics and exhibits fast subthreshold oscillation of membrane potential. Due to such dynamics, the behavior of the RFN circuit is sensitive to the timing of stimuli. We investigated the effects of the sensitivity and the mutual interaction on the dynamic behavior of two pulse-coupled RFN circuits, and will demonstrate out of phase burst synchronization and bifurcation phenomena through circuit simulations.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Bayesian inference with an adaptive proposal density for GARCH models

We perform the Bayesian inference of a GARCH model by the Metropolis-Hastings algorithm with an adaptive proposal density. The adaptive proposal density is assumed to be the Student's t-distribution and the distribution parameters are evaluated by using the data sampled during the simulation. We apply the method for the QGARCH model which is one of asymmetric GARCH models and make empirical studies for for Nikkei 225, DAX and Hang indexes. We find that autocorrelation times from our method are very small, thus the method is very efficient for generating uncorrelated Monte Carlo data. The results from the QGARCH model show that all the three indexes show the leverage effect, i.e. the volatility is high after negative observations

A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate

In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities. However, the integration density of present neuromorphic devices is much less than that of human brains. In this report, we present molecular neuromorphic devices, composed of a dynamic and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). We show experimentally that the SWNT/POM network generates spontaneous spikes and noise. We propose electron-cascading models of the network consisting of heterogeneous molecular junctions that yields results in good agreement with the experimental results. Rudimentary learning ability of the network is illustrated by introducing reservoir computing, which utilises spiking dynamics and a certain degree of network complexity. These results indicate the possibility that complex functional networks can be constructed using molecular devices, and contribute to the development of neuromorphic devices