Hypersausage neural networks and its application in face recognition

In this paper, we firstly give the nature of 'hypersausages', study its structure and training of the network, then discuss the nature of it by way of experimenting with ORL face database, and finally, verify its unsurpassable advantages compared with other means.

Circuit design and simulation of a CMOS-based preamplifier for brain neural signals

A novel CMOS-based preamplifier for amplifying brain neural signal obtained by scalp electrodes in brain-computer interface (BCI) is presented in this paper. By means of constructing effective equivalent input circuit structure of the preamplifier, two capacitors of 5 pF are included to realize the DC suppression compared to conventional preamplifiers. Then this preamplifier is designed and simulated using the standard 0.6 mu m MOS process technology model parameters with a supply voltage of 5 volts. With differential input structures adopted, simulation results of the preamplifier show that the input impedance amounts to more than 2 Gohm with brain neural signal frequency of 0.5 Hz-100 Hz. The equivalent input noise voltage is 18 nV/Hz(1/2). The common mode rejection ratio (CMRR) of 112 dB and the open-loop differential gain of 90 dB are achieved.

on string languages generated by spiking neural p systems

We continue the study of spiking neural P systems by considering these computing devices as binary string generators: the set of spike trains of halting computations of a given system constitutes the language generated by that system. Although the "direct" generative capacity of spiking neural P systems is rather restricted (some very simple languages cannot be generated in this framework), regular languages are inverse-morphic images of languages of finite spiking neural P systems, and recursively enumerable languages are projections of inverse-morphic images of languages generated by spiking neural P systems.

Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks

Enzymatic hydrolysis of cellulose was highly complex because of the unclear enzymatic mechanism and many factors that affect the heterogeneous system. Therefore, it is difficult to build a theoretical model to study cellulose hydrolysis by cellulase. Artificial neural network (ANN) was used to simulate and predict this enzymatic reaction and compared with the response surface model (RSM). The independent variables were cellulase amount X-1, substrate concentration X-2, and reaction time X-3, and the response variables were reducing sugar concentration Y-1 and transformation rate of the raw material Y-2. The experimental results showed that ANN was much more suitable for studying the kinetics of the enzymatic hydrolysis than RSM. During the simulation process, relative errors produced by the ANN model were apparently smaller than that by RSM except one and the central experimental points. During the prediction process, values produced by the ANN model were much closer to the experimental values than that produced by RSM. These showed that ANN is a persuasive tool that can be used for studying the kinetics of cellulose hydrolysis catalyzed by cellulase.

Experimental Investigation into the Production Behavior of Methane Hydrate in Porous Sediment with Hot Brine Stimulation

The gas production behavior from methane hydrate in porous sediment by injecting the brine with the salinity of 0−24 wt % and the temperature of −1 to 130 °C was investigated in a one-dimensional experimental apparatus. The results show that the gas production process consists of three periods: the free gas production, the hydrate dissociation, and the general gas reservoir production. The hydrate dissociation accompanies the temperature decrease with the injection of the brine (NaCl solution), and the dissociation duration is shortened with the increase of the salinity. With the injection of hot brine, instantaneous hydrate dissociation rate also increases with the increase of the salinity. However, while the NaCl concentration is beyond a certain value, the rate has no longer continued increasing. Thermal efficiency and energy ratio for the hydrate production can be enhanced by injecting hot brine, and the enhanced effectiveness is quite good with the injection of high salinity at lower temperature.

Fabrication of a Silicon-Based Microprobe for Neural Interface Applications

A two-dimensional (2D) multi-channel silicon-based microelectrode array is developed for recording neural signals. Three photolithographic masks are utilized in the fabrication process. SEM images show that the microprobe is 1. 2mm long,100μm wide,and 30μm thick, with recording sites spaced 200μm apart for good signal isolation. For the individual recording sites, the characteristics of impedance versus frequency are shown by in vitro testing. The impedance declines from 14MΩ to 1.9kv as the frequency changes from 0 to 10MHz. A compatible PCB (print circuit board) aids in the less troublesome implantation and stabilization of the microprobe.

Simulation of a Monolithic Integrated CMOS Preamplifier for Neural Recordings

A monolithic integrated CMOS preamplifier is presented for neural recording applications. Two AC-coupied capacitors are used to eliminate the large and random DC offsets existing in the electrode-electrolyte interface. Diode-connected nMOS transistors with a negative voltage between the gate and source are candidates for the large resistors necessary for the preamplifier. A novel analysis is given to determine the noise power spectral density. Simulation results show that the two-stage CMOS preamplifier in a closed-loop capacitive feedback configuration provides an AC in-band gain of 38.8dB,a DC gain of 0,and an input-referred noise of 277nVmax, integrated from 0. 1Hz to 1kHz. The preamplifier can eliminate the DC offset voltage and has low input-referred noise by novel circuit configuration and theoretical analysis.

Architecture research and hardware implementation on simplified neural computing system for face identification

This paper describes a special-purpose neural computing system for face identification. The system architecture and hardware implementation are introduced in detail. An algorithm based on biomimetic pattern recognition has been embedded. For the total 1200 tests for face identification, the false rejection rate is 3.7% and the false acceptance rate is 0.7%.

Artificial neural networks expecting more advanced microelectronics technology

　

Global stability of Hopfield neural networks

This paper gives a condition for the global stability of a continuous-time hopfield neural network when its activation function maybe not monotonically increasing.

Effects of Naotan Pill on repair of neural cells and cognitive disorders in juvenile rats following hypoxia and ischemia

BACKGROUND: Hypoxia and ischemia induce neuronal damage, decreased neuronal numbers and synaptophysin levels, and deficits in learning and memory functions. Previous studies have shown that lycium barbarum polysaccharide, the most effective component of barbary wolfberry fruit, has protective effects on neural cells in hypoxia-ischemia. OBJECTIVE: To investigate the effects of Naotan Pill on glutamate-treated neural cells and on cognitive function in juvenile rats following hypoxia-ischemia. DESIGN, TIME AND SETTING: The randomized, controlled, in vivo study was performed at the Cell Laboratory of Lanzhou University, Lanzhou Institute of Modern Physics of Chinese Academy of Sciences, and Department of Traditional Chinese Medicine of Gansu Provincial Rehabilitation Center Hospital, China from December 2005 to August 2006. The cellular neurobiology, in vitro experiment was conducted at the Institute of Human Anatomy, Histology, Embryology and Neuroscience, School of Basic Medical Sciences, Lanzhou University, and Department of Traditional Chinese Medicine of Gansu Provincial Rehabilitation Center Hospital, China from March 2007 to January 2008. MATERIALS: Naotan Pill, composed of barbary wolfberry fruit, danshen root, grassleaf sweetflag rhizome, and glossy privet fruit, was prepared by Gansu Provincial Rehabilitation Center, China. Rabbit anti-synaptophysin, choline acetyl transferase polyclonal antibody, streptavidin-biotin complex kit and diaminobenzidine kit (Boster, Wuhan, China), as well as glutamate (Hualian, Shanghai, China) were used in this study. METHODS: Cortical neural cells were isolated from neonatal Wistar rats. Neural cell damage models were induced using glutamate, and administered Naotan Pill prior to and following damage. A total of 54 juvenile Wistar rats were equally and randomly assigned into model, Naotan Pill, and sham operation groups. The left common carotid artery was ligated, and then rat models of hypoxic-ischemic injury were assigned to the model and Naotan Pill groups. At 2 days following model induction, rats in the Naotan Pill group were administered Naotan Pill suspension for 21 days. In the model and sham operation groups, rats received an equal volume of saline. MAIN OUTCOME MEASURES: Neural cell morphology was observed using an inverted phase contrast microscope. Survival rate of neural cells was measured by MTT assay. Synaptophysin and choline acetyl transferase expression was observed in the hippocampal CA1 region of juvenile rats using immunohistochemistry. Cognitive function was tested by the Morris water maze. RESULTS: Pathological changes were detected in glutamate-treated neural cells. Neural cell morphology remained normal after Naotan Pill intervention. Absorbance and survival rate of neural cells were significantly greater following Naotan Pill intervention, compared to glutamate-treated neural cells (P < 0.05). Synaptophysin and choline acetyl transferase expression was lowest in the hippocampal CA1 region in the model group and highest in the sham operation group. Significant differences among groups were observed (P < 0.05). Escape latency and swimming distance were significantly longer in the model group compared to the Naotan Pill group (P < 0.05). CONCLUSION: Naotan Pill exhibited protective and repair effects on glutamate-treated neural cells. Naotan Pill upregulated synaptophysin and choline acetyl transferase expression in the hippocampus and improved cognitive function in rats following hypoxia-ischemia.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.