Creating the brain and interacting with the brain: an integrated approach to understanding the brain

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the ‘understanding the brain by creating the brain’ approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain–machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.     

Age of acquisition: Its neural and computational mechanisms.

The acquisition of new skills over a life span is a remarkable human ability. This ability, however, is constrained by age of acquisition (AoA); that is, the age at which learning occurs significantly affects the outcome. This is most clearly reflected in domains such as language, music, and athletics. This article provides a perspective on the neural and computational mechanisms underlying AoA in language acquisition. The authors show how AoA modulates both monolingual lexical processing and bilingual language acquisition. They consider the conditions under which syntactic processing and semantic processing may be differentially sensitive to AoA effects in second-language acquisition. The authors conclude that AoA effects are pervasive and that the neural and computational mechanisms underlying learning and sensorimotor integration provide a general account of these effects. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

On the complexity of simulating space-bounded quantum computations

This paper studies the space-complexity of predicting the long-term behavior of a class of stochastic processes based on evolutions and measurements of quantum mechanical systems. These processes generalize a wide range of both quantum and classical space-bounded computations, including unbounded error computations given by machines having algebraic number transition amplitudes or probabilities. It is proved that any space s quantum stochastic process from this class can be simulated probabilistically with unbounded error in space O(s), and therefore deterministically in space O(s²).     

Local Exact Particle Tracing on Unstructured Grids

For analyzing and interpreting results of flow simulations, particle tracing is a well established visualization method. In addition, it is a preliminary step for more advanced techniques such as line integral convolution. For interactive exploration of large data sets, a very efficient and reliable particle tracing method is needed. For wind channel experiments or flight simulations, large unstructured computational grids have become common practice. Traditional approachs, based on numerical integration methods of ordinary differential equations however fail to deliver sufficiently accurate path calculation at the speed required for interactive use. In this paper we extend the local exact approach of Nielson and Jung in such a way that it can be used for interactive particle tracing in large data sets of steady flow simulation experiments. This will be achieved by sophisticated preprocessing using additional memory. For further visual enhancement of the streamline we construct an implicitly defined smooth Bézier curve that is used for ray tracing. This allows us to visualize additional scalar values of the simulation as attributes to the trajectory and enables the display of high‐quality smooth curves without creating any visualization geometry and providing a good impression of the spatial situation at the same time. ACM CSS: I.3.3 Computer Graphics—Line and curve generation; I .3.7 Computer Graphics—Raytracing; G.1.2 Numerical Analysis—Spline and piecewise polynomial approximation     

Modeling language acquisition in atypical phenotypes.

An increasing number of connectionist models have been proposed to explain behavioral deficits in developmental disorders. These simulations motivate serious consideration of the theoretical implications of the claim that a developmental disorder fits within the parameter space of a particular computational model of normal development. The authors examine these issues in depth with respect to a series of new simulations investigating past-tense formation in Williams syndrome. This syndrome and the past-tense domain are highly relevant because both have been used to make strong theoretical claims about the processes underlying normal language acquisition. The authors conclude that computational models have great potential to advance psychologists' understanding of developmental deficits because they focus on the developmental process itself as a pivotal causal factor in producing atypical phenotypic outcomes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

《周培德三角剖分不是最小权三角剖分》一文商榷——反例不成立

周培协三角剖分是否能得到最小权三角剖分，周培德在《周培德三角部分不是最小权三角剖分》一文撰写之前已有新的结论，本文还指出《周培德三角剖分不是最小权三角剖分》一文所举反例不成立。     

A hierarchical model for structure learning based on the physiological characteristics of neurons

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability. The characteristics of typical ANN models are fixed connections, with evolved weights, globalized representations, and globalized optimizations, all based on a mathematical approach. This makes those models to be deficient in robustness, efficiency of learning, capacity, anti-jamming between training sets, and correlativity of samples, etc. In this paper, we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing. A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures. The basic characteristics of this model are localized and random connections, field limitations of neuron fan-in and fan-out, dynamic behavior of neurons, and samples represented through different sub-circuits of neurons specialized into different response patterns. At the end of this paper, some important aspects of error correction, capacity, learning efficiency, and soundness of structural representation are analyzed theoretically. This paper has demonstrated the feasibility and advantages of structure learning and representation. This model can serve as a fundamental element of cognitive systems such as perception and associative memory.     

Stress Concentration Factors for a Circular Hole in Curved Beams Under Bending Loads

Abstract: As a contribution to the field of stress concentration studies, the case of circular hole in curved bar elements under bending is hereby studied. Trend values of stress concentration factors (SCF) are presented based on selected variables of the problem for two critical hole locations. The results will help to identify an SCF law for practical use by applying the statistical method of regression analysis. Two approximations obtained by means of computational methods are compared with those obtained by photoelastic experimental method.     

求解机械装配规划的新方法

本文提出一个求解机械装配规划的算法,其计算量～O(sN~2),其中s是零件所有可能装配方向的个数,N是工件的零件数(一般s～O(N)).而现行的求机械装配规划的算法,其计算量均随N的增加按指数律增加.     

History of the stiffness method

This paper presents a brief history of the development of the stiffness method. We start by tracing the evolution of the method to solve discrete‐type problems such as trusses and frames composed of two node members. We then describe the method as it is applied to solve continuum problems modelled by finite‐difference and finite‐element methods. Copyright © 2006 John Wiley & Sons, Ltd.