Democratic integration: self-organized integration of adaptive cues

Sensory integration or sensor fusion -- the integration of information from different modalities, cues, or sensors -- is among the most fundamental problems of perception in biological and artificial systems. We propose a new architecture for adaptively integrating different cues in a self-organized manner. In Democratic Integration different cues agree on a result, and each cue adapts toward the result agreed on. In particular, discordant cues are quickly suppressed and recalibrated, while cues having been consistent with the result in the recent past are given a higher weight in the future. The architecture is tested in a face tracking scenario. Experiments show its robustness with respect to sudden changes in the environment as long as the changes disrupt only a minority of cues at the same time, although all cues may be disrupted at one time or another.     

Neuronal responses below firing threshold for subthreshold cross-modal enhancement

Multisensory integration (such as somatosensation-vision, gustation-olfaction) could occur even between subthreshold stimuli that in isolation do not reach perceptual awareness. For example, when a somatosensory (subthreshold) stimulus is delivered within a close spatiotemporal congruency, a visual (subthreshold) stimulus evokes a visual percept. Cross-modal enhancement of visual perception is maximal when the somatosensory stimulation precedes the visual one by tens of milliseconds. This rapid modulatory response would not be consistent with a top-down mechanism acting through higher-order multimodal cortical areas, but rather a direct interaction between lower-order unimodal areas. To elucidate the neuronal mechanisms of subthreshold cross-modal enhancement, we simulated a neural network model. In the model, lower unimodal (X, Y) and higher multimodal (M) networks are reciprocally connected by bottom-up and top-down axonal projections. The lower networks are laterally connected with each other. A pair of stimuli was presented to the lower networks, whose respective intensities were too weak to induce salient neuronal activity (population response) when presented alone. Neurons of the Y network were slightly depolarized below firing threshold when a cross-modal stimulus was presented alone to the X network. This allowed the Y network to make a rapid (within tens of milliseconds) population response when presented with a subsequent congruent stimulus. The reaction speed of the Y network was accelerated, provided that the top-down projections were strengthened. We suggest that a subthreshold (nonpopulation) response to a cross-modal stimulus, acting through interaction between lower (primary unisensory) areas, may be essential for a rapid suprathreshold (population) response to a congruent stimulus that follows. Top-down influences on cross-modal enhancement may be faster than expected, accelerating reaction speed to input, in which ongoing-spontaneous subthreshold excitation of lower-order unimodal cells by higher-order multimodal cells may play an active role.     

Forecasting time series with a new architecture for polynomial artificial neural network

Polynomial artificial neural networks (PANN) have been shown to be powerful for forecasting nonlinear time series. The training time is small compared to the time used by other algorithms of artificial neural networks and the capacity to compute relations between the inputs and outputs represented by every term of the polynomial. In this paper a new structure of polynomial is presented that improves the performance of this type of network considering only non-integers exponents. The architecture adaptation uses genetic algorithm (GA) to find the optimal architecture for every example. Some examples of sunspots and chaotic time series are presented.     

面向神经计算的并行机体系结构设计

面向神经计算的并行计算机体系结构是神经网络研究中的一项重要工作。本文在对大量的神经计算进行需求分析的基础上，讨论了以高性能的微处理器作为计算单元，进行面向神经计算的并行计算机体系结构设计，并且介绍了它原型实现的结构、参数和性能     

An integration methodology based on fuzzy inference systems and neural approaches for multi-stage supply-chains

This paper proposes a methodology for supply chain (SC) integration from customers to suppliers through warehouses, retailers, and plants via both adaptive network based fuzzy inference system and artificial neural networks approaches. The methodology presented provides this integration by finding the requested supplier capacities using the demand and order lead time information across the whole SC in an uncertain environment. The SC structure is investigated stage by stage. The sensitivity analysis is made by comparing the obtained results with the traditional statistical techniques. A company serving in durable consumer goods industry that produces consumer electronics in Istanbul, Turkey was examined to demonstrate the applicability of the proposed methodology.     

Cognitive enhancement mediated through postsynaptic actions of norepinephrine on ongoing cortical activity

We propose two distinct types of norepinephrine (NE)-neuromodulatory systems: an enhanced-excitatory and enhanced-inhibitory (E-E/E-I) system and a depressed-excitatory and enhanced-inhibitory (D-E/E-I) system. In both systems, inhibitory synaptic efficacies are enhanced, but excitatory ones are modified in a contradictory manner: the E-E/E-I system enhances excitatory synaptic efficacies, whereas the D-E/E-I system depresses them. The E-E/E-I and D-E/E-I systems altered the dynamic property of ongoing (background) neuronal activity and greatly influenced the cognitive performance (S/N ratio) of a cortical neural network. The E-E/E-I system effectively enhanced S/N ratio for weaker stimuli with lower doses of NE, whereas the D-E/E-I system enhanced stronger stimuli with higher doses of NE. The neural network effectively responded to weaker stimuli if brief gamma-bursts were involved in ongoing neuronal activity that is controlled under the E-E/E-I neuromodulatory system. If the E-E/E-I and the D-E/E-I systems interact within the neural network, depressed neurons whose activity is depressed by NE application have bimodal property. That is, S/N ratio can be enhanced not only for stronger stimuli as its original property but also for weaker stimuli, for which coincidental neuronal firings among enhanced neurons whose activity is enhanced by NE application are essential. We suggest that the recruitment of the depressed neurons for the detection of weaker (subthreshold) stimuli might be advantageous for the brain to cope with a variety of sensory stimuli.     

Assessing the role of attention in the audiovisual integration of speech

Currently, one of the most controversial topics in the study of multisensory integration in humans (and in its implementation in the development of new technologies for human communication systems) concerns the question of whether or not attention is needed during (or can modulate) the integration of sensory signals that are presented in different sensory modalities. Here, we review the evidence on this question, focusing specifically on the integration of auditory and visual information during the perception of speech. Contrary to the mainstream view that has been prevalent for the last 30 years or so, recent studies have now started to reveal that attentional resources are, in fact, recruited during audiovisual multisensory integration, at least under certain conditions. Finally, considering all of the available evidence, we discuss the extent to which audiovisual speech perception should be considered to represent a ‘special’ case of audiovisual, and more generally, of multisensory integration.     

基于XML的异构数据库信息集成的研究与实现

文章介绍了XML技术及其特点,并将此技术运用于异构网络数据库信息集成的设计,提出了基于XML的信息系统集成体系结构,给出了基于该体系结构的一个具体实例。     

Speeding up backpropagation using multiobjective evolutionary algorithms

The use of backpropagation for training artificial neural networks (ANNs) is usually associated with a long training process. The user needs to experiment with a number of network architectures; with larger networks, more computational cost in terms of training time is required. The objective of this letter is to present an optimization algorithm, comprising a multiobjective evolutionary algorithm and a gradient-based local search. In the rest of the letter, this is referred to as the memetic Pareto artificial neural network algorithm for training ANNs. The evolutionary approach is used to train the network and simultaneously optimize its architecture. The result is a set of networks, with each network in the set attempting to optimize both the training error and the architecture. We also present a self-adaptive version with lower computational cost. We show empirically that the proposed method is capable of reducing the training time compared to gradient-based techniques.     

Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms

We describe in this paper a comparative study between fuzzy inference systems as methods of integration in modular neural networks for multimodal biometry. These methods of integration are based on techniques of type-1 fuzzy logic and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms with the goal of having optimized versions of both types of fuzzy systems. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms can generate fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimized fuzzy systems of integration. The comparative study of the type-1 and type-2 fuzzy inference systems was made to observe the behavior of the two different integration methods for modular neural networks for multimodal biometry.     

The application of a neurocomputer for a control problem

Most artificial neural networks (ANN) are realized on a conventional computer which generally has only one CPU for general routines. In this paper, we used the neurocomputer called MY-NEUPOWER, produced by Hitachi Microcomputer System Ltd., Tokyo, Japan, to carry out research on artificial neural networks. We developed application software for a multilayer feedforward neural network to approximate the nonlinear function of the cooler system in a steel plant. The training algorithm is a type of improved back propagation (BP) algorithm with a variable training coeffcient. The software was programmed on the UNIX platform using the parallel language MY-PARAL. The simulation result demonstrated the merit of the neurocomputer, i.e., the conciseness and simplicity of the program, and the short calculating time. The result also showed that the architecture of neural networks is suitable, and the output error of a neural network is within 5%. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

A recurrent network for dynamic system identification

This paper presents a type of recurrent artificial neural network architecture for identification of an arbitrary, continuous dynamic system. The recurrent network is shown to be stable for a constant input with certain conditions on the parameters of the network. The proposed network has significant advantages over similar models in continuous time nonlinear system identification and is used to identify three nonlinear dynamic systems. Finally, the applicability of the radial basis function networks using the same network architecture to reduce the time-complexity of the training task is presented.     

基于属性整合的知觉模式生成与识别模型

在［３］的基础上，本文给出了基于属性整合的模式生成与转化机制的数学表达，并对跨感觉模式识别中的特征辨识和模式匹配、补充和修正问题，给出了相应的评判函数     

Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in Taiwan

Supplying industrial firms with an accurate method of forecasting the production value of the mechanical industry to facilitate decision makers in precise planning is highly desirable. Numerous methods, including the autoregressive integrated-moving average (ARIMA) model and artificial neural networks can make accurate forecasts based on historical data. The seasonal ARIMA (SARIMA) model and artificial neural networks can also handle data involving trends and seasonality. Although neural networks can make predictions, deciding the most appropriate input data, network structure and learning parameters are difficult. Therefore, this article presents a hybrid forecasting method that combines the SARIMA model and neural networks with genetic algorithms. Analytical results generated by the SARIMA model are inputted as the input data of a neural network. Subsequently, the number of neurons in the hidden layer and the number of learning parameters of the neural network architecture are globally optimized using genetic algorithms. This model is subsequently adopted to forecast seasonal time series data of the production value of the mechanical industry in Taiwan. The results presented here provide a valuable reference for decision makers in industry.     

A Dynamic Network Model of the Color Visual Pathways for Attentive Recognition

A neural network architecture for the segmentation and recognition of colored and textured visual stimuli is presented. The architecture is based on the Boundary Contour System and Feature Contour System (BCS/FCS) of S. Grossberg and E. Mingolla. The architecture proposes a biologically-inspired mechanism for color processing based on antagonist interactions. It suggests how information from different modalities (i.e. color or texture) can be fused together to form a coherent segmentation of the visual scene. It identifies two stages of visual pattern recognition, namely, a global preattentive recognition of the visual scene followed by a local attentive recognition within a particular visual context. The global and local classification and recognition of visual stimuli use ART-type models of G. Carpenter and S. Grossberg for pattern learning and recognition based on color and texture. One example is presented corresponding to an figure-figure separation task. The architecture provides a mechanism for segmentation, categorization and recognition of images from different classes based on self-organizing principles of perception and pattern recognition.     

Finding the Latent Semantics of Haptic Interaction Research: A Systematic Literature Review of Haptic Interaction Using Content Analysis and Network Analysis

As interest in multimodal and tangible interfaces is increasing in the field of human–robot interaction and virtual reality, haptics has been researched across areas such as engineering, computer science, psychology, and neuroscience. The main objective of the study was to construct a comprehensive review of the current haptic‐related literature based on quantitative data derived from content analysis and network analysis. Using the results of content analysis and network analysis of 6,000 research articles on haptic interaction, the haptic‐related literature was classified into two categories: 1) studies on technologies providing haptic stimuli and 2) studies on the human perception of haptic stimuli. Emotions in haptic feedback and haptic perception characteristics of various body sites were identified as potential research topics for further investigation. Greater research effort on understanding human haptic sensation and perception using the proposed systematic approach could accelerate the development of haptic interaction technology.     

软件定义天地一体化网络接入认证架构与方法

天地一体化网络因结构复杂存在网络异构动态、间歇连通、节点高度暴露等特性，为了保证安全，需要研究专有的接入认证架构与方法。首先利用软件定义网络控制面与数据面分离的思想将它与天地一体化信息网络相结合提出一种新的接入认证架构，对认证架构与过程进行详细描述，可以实现对网络的安全防护与资源的优化控制。然后根据架构特点，提出7个影响接入点决策的属性，给出各个属性的计算公式，将层次分析法与逼近理想解的排序方法相结合提出一种接入点决策算法，实验仿真结果表明接入点决策准确，可以实现资源的合理利用。     

基于模糊神经网络的人工鱼虚拟味觉系统研究

本文设计了在智能虚拟环境下人工鱼的一种虚拟味觉系统.利用模糊神经网络实现了鱼儿对食物的学习记忆算法.模糊神经网络由于同时具备了模糊逻辑对规则的表达能力以及神经网络的学习能力,非常适合解决虚拟环境中味觉的信号识别问题,经实验验证是切实可行的.基于模糊神经网络的味觉系统的研究和实现为人工鱼多感知融合系统提供了基础.     

fMRI的视觉神经信息编解码方法综述

视觉神经信息编解码旨在利用功能磁共振成像(functional magnetic resonance imaging,fMRI)等神经影像数据研究视觉刺激与大脑神经活动之间的关系。编码研究可以对神经活动模式进行建模和预测,有助于脑科学与类脑智能的发展;解码研究可以对人的视知觉状态进行解译,能够促进脑机接口领域的发展。因此,基于fMRI的视觉神经信息编解码方法研究具有重要的科学意义和工程价值。本文在总结基于fMRI的视觉神经信息编解码关键技术与研究进展的基础上,分析现有视觉神经信息编解码方法的局限。在视觉神经信息编码方面,详细介绍了基于群体感受野估计方法的发展过程;在视觉神经信息解码方面,首先,按照任务类型将其划分为语义分类、图像辨识和图像重建3个部分,并深入阐述了每个部分的代表性研究工作和所用的方法。特别地,在图像重建部分着重介绍了基于深度生成模型(主要包括变分自编码器和生成对抗网络)的简单图像、人脸图像和复杂自然图像的重建技术。其次,统计整理了该领域常用的10个开源数据集,并对数据集的样本规模、被试个数、刺激类型、研究用途及下载地址进行了详细归纳。最后,详细介绍了视觉神经信息编解码模...     

自适应神经网络学习方法研究

本文从连接权值、网络的拓扑结构、网络的学习参数以及神经元的激活特性等不同方面分别讨论了人工神经网络的学习问题，并就当前流行的ＢＰ模型提出了具体实现方法。实验表明，这些方法对于加快网络的收敛速度，优化网络的拓扑结构等方面有着显著成效，本文所述内容为ＡＮＮ学习算法的改进与设计提供了示例，途径和思想总结。