用GESA方法求解任务安排问题

ＧＥＳＡ方法是一种并行算法，它以一种新颖的方式综合了遗传算法，模拟退火（ｓｉｍｕｌａｔｅｄａｎｎｅａｌｉｎｇ）模拟进化（ｓｉｎｕｌａｔｅｄｅｖｏｌｕｔｉｏｎ）的思想，特别是ＧＥＳＡ方法中实施了区域引导了（ｒｅｇｉｏｎａｌｇｕｉｄａｎｃｅ），用ＧＥＳＡ方法求解任务安排问题，结果表明ＧＥＳＡ方法性能优越。     

油藏数值模拟管理平台(PNSMP)的设计与实现

该文简要介绍了在 ＳＵＮ工作站 Ｓｏｌａｒｉｓ环境中研制开发的油藏数值模拟管理平台软件 ＰＮＳＭＰ,它主要包括胜利黑油、多层二维二相及稠油热采等三部分。文章对油藏数值模拟管理平台的研究方法、研制思路及其开发工具进行了探讨,对于开发Ｍｏｔｉｆ风格的图形用户界面的成功经验进行了总结,并介绍了一种汉化界面的实用的简便方法。     

用微机实现的STD工控机图形开发环境及工具

本文针对ＳＴＤ５８０３系列工控机在图形界面开发上存在的缺陷，提出一种用微机实现的ＳＴＤ工控机图形开发环境。在这个图形开发环境中，用户可以直接在微机中调试ＳＴＤ工控机图形程序，并能在微机模拟ＳＴＤ工控机的显示效果；另外本文还提供一个面向高级语言的ＳＴＤ图形函数库，使得高级语言进行ＳＴＤ图形开发更加方便快捷。     

像素阵列转化为S树的并行算法及其实现

本文提出了一种从像素阵列到Ｓ树转换的并行法及其在曙光１０００上的具体实现。它是采用叶码和树码相结合的方法对图像进行压缩编码的。这是一种对大型图像的有效表示方法，对图像的存储也十分有效。该算法首先采用模拟遍历线性树的方法把二值图像转换成ＦＤ位置码，然后再把ＦＤ位置码转化成Ｓ树。同以往的树形编码相比Ｓ树具有较高的压缩比、较好的压缩速度。该算法串行时间复杂度是Ｏ（ｎ＾２），用Ｐ个处理器可在Ｏ（ｎ＾２／Ｐ     

ABC-90jr模拟器的设计与实现

ＡＢＣ－９０ｊｒ是一台ＳＩＭＤ型的阵列机的原理样机，它将面向图象处理、信号处理等细粒度的并行计算。本文介绍了在微机上对这种机器所实施的一种软件模拟的方法及实现的过程，并用了一个实例验证了这种模拟的正确性     

一种实用V／T转换器的实现

一种实用Ｖ／Ｔ转换器的实现苏红旗，吕松棠ＴｈｅＲｅａｌｉｚａｔｉｏｎｏｆａＴｙｐｅｏｆＰｒａｃｔｉｃａｌＶ／ＴＴｒａｎｓｆｏｒｍｅｒ￥ＳｕＨｏｎｇｑｉ；ＬｕＳｏｎｇｔａｎｇ１引言Ｖ／Ｔ转换又称为电压一脉宽变换，它是将模拟电压信号变成脉宽信号，进而可以...     

SICE：一个SIMD计算机程序设计模拟环境

本文实现的ＳＩＣＥ（ＳＩＭＤＣＥｍｕｌａｔｏｒ）是一个在串行机的环境下模拟进行ＳＩＭＤ计算机程序设计的软件包。ＳＩＣ（ＳＩＭＤＣ）是作者定义的一种基于Ｃ语言的ＳＩＭＤ并行扩展语言，它一方面支持反映ＳＩＭＤ结构特点的的并行语句，更重要的是可支持ＳＩＭＤ结构的定义，能方便的用于ＳＩＭＤ机器的算法研究。     

同步与数据缓冲系统同步定时器

同步定时器是ＣＤＡＳＳ／ＤＢ系统的重要部分，它用高精度数字锁相环，精确地恢复地球同步气象卫星原始云图数据采集的同步基准信息。数字锁相环由高速器件组成的相位比较器和用计算机软件实现的滤波器构成，采用标准频率计数方式，完成模拟太阳（ＳＰ）和数字太阳（ＳＳＤ）的锁相及星上时钟频率测量等任务。     

多重模拟光带显示器

介绍了一种以ＭＣＳ－５１系列单片机为核心的，能对输入模拟量进行数字和模拟显示的新型光带显示器，讨论了它的硬件结构和软件设计。     

用三束磁光盘驱动器的自动光学海量存储系统

一种高速磁光盘驱动器的自动光学海量存储系统（ＭＳＳ）业已研制开发．它的特点是：采用１３０ｍｍＩＳＯ标准磁光盘，有很高的写入数据传输率和高效存储盘盒。作为关键装置──一种高速磁光盘驱动器已经研制出来。这种驱动器提供了高速数据写入容量，它比平常采用的磁光盘驱动器约大１０倍．这种光学ＭＳＳ写入和读出数据传输率为２．１ＭＢ／ｓ，存储容量为２５０ＧＢ～１ＴＢ，盘盒平均处理时间为５ｓ。根据性能模拟，业已证实，光学ＭＳＳ可用于贮存多媒体数据的低通信量的随机存储文件和高速ＤＡＳＤ后备文件．     

Event-driven simulation of spiking neurons with stochastic dynamics

We present a new technique, based on a proposed event-based strategy (Mattia & Del Giudice, 2000), for efficiently simulating large networks of simple model neurons. The strategy was based on the fact that interactions among neurons occur by means of events that are well localized in time (the action potentials) and relatively rare. In the interval between two of these events, the state variables associated with a model neuron or a synapse evolved deterministically and in a predictable way. Here, we extend the event-driven simulation strategy to the case in which the dynamics of the state variables in the inter-event intervals are stochastic. This extension captures both the situation in which the simulated neurons are inherently noisy and the case in which they are embedded in a very large network and receive a huge number of random synaptic inputs. We show how to effectively include the impact of large background populations into neuronal dynamics by means of the numerical evaluation of the statistical properties of single-model neurons under random current injection. The new simulation strategy allows the study of networks of interacting neurons with an arbitrary number of external afferents and inherent stochastic dynamics.     

Neural network model to generate head swing in locomotion of Caenorhabditis elegans

Computer simulation of the neural network composed of the head neurons of Caenorhabditis elegans was performed to reconstruct the realistic changes in the membrane potential of motoneurons in swinging the head for coordinated forward locomotion. The model neuron had ion channels for calcium and potassium, whose parameters were obtained by fitting the experimental data. Transmission properties of the chemical synapses were set as graded. The neural network involved in forward movement was extracted by tracing the neuronal activity flow upstream from the motoneurons connected to the head muscles. Simulations were performed with datasets, which included all combinations of the excitatory and inhibitory properties of the neurons. In this model, a pulse input entered only from motoneuron VB1, and activation of the stretch receptors on SAA neurons was necessary for the periodic bending. The synaptic output property of each neuron was estimated for the alternate contraction of the dorsal and ventral muscles. The AIB neuron was excitatory, RIV and SMD neurons seemed to be excitatory and RMD and SAA neurons seemed to be inhibitory. With datasets violating Dale's principle for the SMB neuron, AIB neuron was excitatory and RMD neuron was inhibitory. RIA, RIV and SMD neurons seemed to be excitatory.     

精细神经网络仿真方法研究进展

树突对大脑神经元实现不同的信息处理功能有着重要作用。精细神经元模型是一种对神经元树突以及离子通道的信息处理过程进行精细建模的模型,可以帮助科学家在实验条件的限制之外探索树突信息处理的特性。由精细神经元组成的精细神经网络模型可通过仿真对大脑的信息处理过程进行模拟,对于理解树突的信息处理机制、大脑神经网络功能背后的计算机理具有重要作用。然而,精细神经网络仿真需要进行大量计算,如何对精细神经网络进行高效仿真是一个具有挑战的研究问题。本文对精细神经网络仿真方法进行梳理,介绍了现有主流仿真平台与核心仿真算法,以及可进一步提升仿真效率的高效仿真方法。将具有代表性的高效仿真方法按照发展历程以及核心思路分为网络尺度并行方法、神经元尺度并行方法以及基于GPU(graphics processing unit)的并行仿真方法3类。对各类方法的核心思路进行总结,并对各类方法中代表性工作的细节进行分析介绍。随后对各类方法所具有的优劣势进行分析对比,对一些经典方法进行总结。最后根据高效仿真方法的发展趋势,对未来研究工作进行展望。     

Spiking神经元输入脉冲扰动敏感性研究

脉冲神经网络是一种基于生物的网络模型,它的输入输出为具有时间特性的脉冲序列,其运行机制相比其他传统人工神经网络更加接近于生物神经网络。神经元之间通过脉冲序列传递信息,这些信息通过脉冲的激发时间编码能够更有效地发挥网络的学习性能。脉冲神经元的时间特性导致了其工作机制较为复杂,而spiking神经元的敏感性反映了当神经元输入发生扰动时输出的spike的变化情况,可以作为研究神经元内部工作机制的工具。不同于传统的神经网络,spiking神经元敏感性定义为输出脉冲的变化时刻个数与运行时间长度的比值,能直接反映出输入扰动对输出的影响程度。通过对不同形式的输入扰动敏感性的分析,可以看出spiking神经元的敏感性较为复杂,当全体突触发生扰动时,神经元为定值,而当部分突触发生扰动时,不同突触的扰动会导致不同大小的神经元敏感性。     

Population density methods for large-scale modelling of neuronal networks with realistic synaptic kinetics: cutting the dimension down to size

Population density methods provide promising time-saving alternatives to direct Monte Carlo simulations of neuronal network activity, in which one tracks the state of thousands of individual neurons and synapses. A population density method has been found to be roughly a hundred times faster than direct simulation for various test networks of integrate-and-fire model neurons with instantaneous excitatory and inhibitory post-synaptic conductances. In this method, neurons are grouped into large populations of similar neurons. For each population, one calculates the evolution of a probability density function (PDF) which describes the distribution of neurons over state space. The population firing rate is then given by the total flux of probability across the threshold voltage for firing an action potential. Extending the method beyond instantaneous synapses is necessary for obtaining accurate results, because synaptic kinetics play an important role in network dynamics. Embellishments incorporating more realistic synaptic kinetics for the underlying neuron model increase the dimension of the PDF, which was one-dimensional in the instantaneous synapse case. This increase in dimension causes a substantial increase in computation time to find the exact PDF, decreasing the computational speed advantage of the population density method over direct Monte Carlo simulation. We report here on a one-dimensional model of the PDF for neurons with arbitrary synaptic kinetics. The method is more accurate than the mean-field method in the steady state, where the mean-field approximation works best, and also under dynamic-stimulus conditions. The method is much faster than direct simulations. Limitations of the method are demonstrated, and possible improvements are discussed.     

Phase synchronizing in Hindmarsh-Rose neural networks with delayed chemical coupling

Although diffusive electrical connections in neuronal networks are instantaneous, excitatory/inhibitory couplings via chemical synapses encompass a transmission time-delay. In this paper neural networks with instantaneous electrical couplings and time-delayed excitatory/inhibitory chemical connections are considered and scaling of the spike phase synchronization with the unified time-delay in the network is investigated. The findings revealed that in both excitatory and inhibitory chemical connections, the phase synchronization could be enhanced by introducing time-delay. The role of the variability of the neuronal external current in the phase synchronization is also investigated. As individual neuron models, Hindmarsh-Rose model is adopted and the network structure of the electrical and chemical connections is considered to be Watts-Strogatz and directed random networks, respectively.     

A multi‐GPU algorithm for large‐scale neuronal networks

Large‐scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low‐cost solution for many high‐performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large‐scale networks composed of biologically realistic Hodgkin–Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad‐core CPU. Copyright © 2010 John Wiley & Sons, Ltd.     

A low-order model of biological neural networks

A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.     

The high-conductance state of cortical networks

We studied the dynamics of large networks of spiking neurons with conductance-based (nonlinear) synapses and compared them to networks with current-based (linear) synapses. For systems with sparse and inhibition-dominated recurrent connectivity, weak external inputs induced asynchronous irregular firing at low rates. Membrane potentials fluctuated a few millivolts below threshold, and membrane conductances were increased by a factor 2 to 5 with respect to the resting state. This combination of parameters characterizes the ongoing spiking activity typically recorded in the cortex in vivo. Many aspects of the asynchronous irregular state in conductance-based networks could be sufficiently well characterized with a simple numerical mean field approach. In particular, it correctly predicted an intriguing property of conductance-based networks that does not appear to be shared by current-based models: they exhibit states of low-rate asynchronous irregular activity that persist for some period of time even in the absence of external inputs and without cortical pacemakers. Simulations of larger networks (up to 350,000 neurons) demonstrated that the survival time of self-sustained activity increases exponentially with network size.     

Pattern recognition in a compartmental model of a CA1 pyramidal neuron.

Computer simulation of a CA1 hippocampal pyramidal neuron is used to estimate the effects of synaptic and spatio-temporal noise on such a cell's ability to accurately calculate the weighted sum of its inputs, presented in the form of transient patterns of activity. Comparison is made between the pattern recognition capability of the cell in the presence of this noise and that of a noise-free computing unit in an artificial neural network model of a heteroassociative memory. Spatio-temporal noise due to the spatial distribution of synaptic input and quantal variance at each synapse degrade the accuracy of signal integration and consequently reduce pattern recognition performance in the cell. It is shown here that a certain degree of asynchrony in action potential arrival at different synapses, however, can improve signal integration. Signal amplification by voltage-dependent conductances in the dendrites, provided by synaptic NMDA receptors, and sodium and calcium ion channels, also improves integration and pattern recognition. While the biological sources of noise are significant when few patterns are stored in the associative memory of which the cell is a part, when large numbers of patterns are stored the noise from the other stored patterns comes to dominate the pattern recognition process. In this situation, the pattern recognition performance of the pyramidal cell is within a factor of two of that of the computing unit in the artificial neural network model.