Islet-1 is required for ventral neuron survival in Xenopus

Islet-1 is a LIM domain transcription factor involved in several processes of embryonic development. Xenopus Islet-1 (Xisl-1) has been shown to be crucial for proper heart development. Here we show that Xisl-1 and Xisl-2 are differentially expressed in th

Telemetric recordings of single neuron activity and visual scenes in monkeys walking in an open field

This paper describes a portable recording system and methods for obtaining chronic recordings of single units and tracking rhesus monkey behavior in an open field. The integrated system consists of four major components: (1) microelectrode assembly; (2) h

A laboratory-scale buried charge simulator

An experimental technique has been developed in order to mimic the effect of landmine loading on materials and structures to be studied in a laboratory setting, without the need for explosives. Compressed gas is discharged beneath a sand layer, simulating the dynamic flow generated by a buried explosive. High speed photography reveals that the stages of soil motion observed during a landmine blast are replicated. The effect of soil saturation and the depth of the sand layer on sand motion are evaluated. Two series of experiments have been performed with the buried charge simulator to characterise subsequent impact of the sand. First, the time variation in pressure and impulse during sand impact on a stationary target is evaluated using a Kolsky bar apparatus. It is found that the pressure pulse imparted to the Kolsky bar consists of two phases: an initial transient phase of high pressure (attributed to wave propagation effects in the impacting sand), followed by a lower pressure phase of longer duration (due to lateral flow of the sand against the Kolsky bar). Both phases make a significant contribution to the total imparted impulse. It is found that wet sand exerts higher peak pressures and imparts a larger total impulse than dry sand. The level of imparted impulse is determined as a function of sand depth, and of stand-off distance between the sand and the impacted end of the Kolsky bar. The second study uses a vertical impulse pendulum to measure the momentum imparted by sand impact to a target which is free to move vertically. The effect of target mass upon imparted momentum is investigated. It is concluded that the laboratory-scale sand impact apparatus is a flexible tool for investigating the interactions between structures and dynamic sand flows. © 2013 Elsevier Ltd. All rights reserved.

Identification of the steering control behaviour of five test subjects following a randomly curving path in a driving simulator

The paper is concerned with the identification of theoretical preview steering controllers using data obtained from five test subjects in a fixed-base driving simulator. An understanding of human steering control behaviour is relevant to the design of autonomous and semi-autonomous vehicle controls. The driving task involved steering a linear vehicle along a randomly curving path. The theoretical steering controllers identified from the data were based on optimal linear preview control. A direct-identification method was used, and the steering controllers were identified so that the predicted steering angle matched as closely as possible the measured steering angle of the test subjects. It was found that identification of the driver's time delay and noise is necessary to avoid bias in identification of the controller parameters. Most subjects' steering behaviour was predicted well by a theoretical controller based on the lateral/yaw dynamics of the vehicle. There was some evidence that an inexperienced driver's steering action was better represented by a controller based on a simpler model of the vehicle dynamics, perhaps reflecting incomplete learning by the driver. Copyright © 2014 Inderscience Enterprises Ltd.

The multi-weight neuron with geometry algorithm and its application

A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multi-weight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.

Double synaptic weight neuron theory and its application

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

Application of multi-weighted neuron for iris recognition

In this paper, from the cognition science point of view, we constructed a neuron of multi-weighted neural network, and proposed a new method for iris recognition based on multi-weighted neuron. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the correct rejection rate is 98.9%, the correct cognition rate and the error recognition rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high. It proves the proposed method for iris recognition is effective.

Discussion on the basic mathematical models of neurons - Double synaptic weight neuron in general-purpose neurocomputer, theory and application

Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.

A more complex neuron in biomimetic pattern recognition

Biomimetic pattern recognition has been proposed for several years, but the discussion of its neuron was not very wide and deep. In this paper, we propose a new more complex neuron named M-neuron and give the application in the last part of the paper.