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1.
We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moderate-precision analog units to collectively compute a precise answer to a computation. Second, frequent discrete signal restoration of the analog information prevents analog noise and offset from degrading the computation. And, third, a state machine enables complex computations to be created using a sequence of elementary computations. A natural choice for implementing this hybrid scheme is one based on spikes because spike-count codes are digital, while spike-time codes are analog. We illustrate how spikes afford easy ways to implement all three components of scalable hybrid computation. First, as an important example of distributed analog computation, we show how spikes can create a distributed modular representation of an analog number by implementing digital carry interactions between spiking analog neurons. Second, we show how signal restoration may be performed by recursive spike-count quantization of spike-time codes. And, third, we use spikes from an analog dynamical system to trigger state transitions in a digital dynamical system, which reconfigures the analog dynamical system using a binary control vector; such feedback interactions between analog and digital dynamical systems create a hybrid state machine (HSM). The HSM extends and expands the concept of a digital finite-state-machine to the hybrid domain. We present experimental data from a two-neuron HSM on a chip that implements error-correcting analog-to-digital conversion with the concurrent use of spike-time and spike-count codes. We also present experimental data from silicon circuits that implement HSM-based pattern recognition using spike-time synchrony. We outline how HSMs may be used to perform learning, vector quantization, spike pattern recognition and generation, and how they may be reconfigured.  相似文献   

2.
Polychronization: computation with spikes   总被引:10,自引:0,他引:10  
We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. Due to the interplay between the delays and STDP, the spiking neurons spontaneously self-organize into groups and generate patterns of stereotypical polychronous activity. To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. We speculate on the significance of polychrony to the theory of neuronal group selection (TNGS, neural Darwinism), cognitive neural computations, binding and gamma rhythm, mechanisms of attention, and consciousness as "attention to memories."  相似文献   

3.
4.
本文系统地讨论了离散型前向神经网络Madaline的输出对网络参数扰动的敏感性计算.首先,根据神经元Adaline的离散特性,提出了一种离散随机技术方法,推导了Adaline敏感性近似计算公式.该方法从理论上解决了已有的连续随机技术方法无法处理的情况,取消了过强的限制条件,并使得从理论上对Adaline敏感性近似计算进...  相似文献   

5.
基于矩阵运算的公交查询高效算法   总被引:3,自引:0,他引:3       下载免费PDF全文
目前绝大多数公交查询算法是基于最短路径查找算法的改进,但最短路径算法本质上不适合公交线路的查询。定义一种新型的直达矩阵,并提出两种新的矩阵运算。在此基础上,建立起了一种基于矩阵运算的高效公交查询算法。对算法进行分析后,引入了一些重要的改进。最后在计算机中把提出的算法应用到实际数据,取得了理想的效果。  相似文献   

6.
Information encoding and computation with spikes and bursts   总被引:3,自引:0,他引:3  
Neurons compute and communicate by transforming synaptic input patterns into output spike trains. The nature of this transformation depends crucially on the properties of voltage-gated conductances in neuronal membranes. These intrinsic membrane conductances can enable neurons to generate different spike patterns including brief, high-frequency bursts that are commonly observed in a variety of brain regions. Here we examine how the membrane conductances that generate bursts affect neural computation and encoding. We simulated a bursting neuron model driven by random current input signal and superposed noise. We consider two issues: the timing reliability of different spike patterns and the computation performed by the neuron. Statistical analysis of the simulated spike trains shows that the timing of bursts is much more precise than the timing of single spikes. Furthermore, the number of spikes per burst is highly robust to noise. Next we considered the computation performed by the neuron: how different features of the input current are mapped into specific output spike patterns. Dimensional reduction and statistical classification techniques were used to determine the stimulus features triggering different firing patterns. Our main result is that spikes, and bursts of different durations, code for different stimulus features, which can be quantified without a priori assumptions about those features. These findings lead us to propose that the biophysical mechanisms of spike generation enables individual neurons to encode different stimulus features into distinct spike patterns.  相似文献   

7.
In a number of emerging streaming applications, the data values that are produced have an associated time interval for which they are valid. A useful computation over such streaming data is to produce a continuous and valid skyline summary. Previous work on skyline algorithms have only focused on evaluating skylines over static data sets, and there are no known algorithms for skyline computation in the continuous setting. In this paper, we introduce the continuous time-interval skyline operator, which continuously computes the current skyline over a data stream. We present a new algorithm called LookOut for evaluating such queries efficiently, and empirically demonstrate the scalability of this algorithm. In addition, we also examine the effect of the underlying spatial index structure when evaluating skylines. Whereas previous work on skyline computations have only considered using the R-tree index structure, we show that for skyline computations using an underlying quadtree has significant performance benefits over an R-tree index.  相似文献   

8.
Transaction-time databases support access to not only the current database state, but also previous database states. Supporting access to previous database states requires large quantities of data and necessitates efficient temporal query processing techniques. Previously, we presented a log based storage structure and algorithms for the differential computation of previous database states. Timeslices-i.e., previous database states-are computed by traversing a log of database changes, using previously computed and cached timeslices as outsets. When computing a new timeslice, the cache will contain two candidate outsets: an earlier outset and a later outset. The new timeslice can be computed by either incrementally updating the earlier outset or decrementally “downdating” the later outset using the log. The cost of this computation is determined by the size of the log between the outset and the new timeslice. The paper proposes an efficient algorithm that identifies the cheaper outset for the differential computation. The basic idea is to compute the sizes of the two pieces of the log by maintaining and using a tree structure on the timestamps of the database changes in the log. The lack of a homogeneous node structure, a controllable and high fill factor for nodes, and of appropriate node allocation in existing tree structures (e.g., B+ trees, Monotonic B+ trees, and Append only trees) render existing tree structures unsuited for our use. Consequently, a specialized tree structure, the pointer-less insertion tree, is developed to support the algorithm. As a proof of concept, we have implemented a main memory version of the algorithm and its tree structure  相似文献   

9.
Nonstationary acoustic features provide essential cues for many auditory tasks, including sound localization, auditory stream analysis, and speech recognition. These features can best be characterized relative to a precise point in time, such as the onset of a sound or the beginning of a harmonic periodicity. Extracting these types of features is a difficult problem. Part of the difficulty is that with standard block-based signal analysis methods, the representation is sensitive to the arbitrary alignment of the blocks with respect to the signal. Convolutional techniques such as shift-invariant transformations can reduce this sensitivity, but these do not yield a code that is efficient, that is, one that forms a nonredundant representation of the underlying structure. Here, we develop a non-block-based method for signal representation that is both time relative and efficient. Signals are represented using a linear superposition of time-shiftable kernel functions, each with an associated magnitude and temporal position. Signal decomposition in this method is a non-linear process that consists of optimizing the kernel function scaling coefficients and temporal positions to form an efficient, shift-invariant representation. We demonstrate the properties of this representation for the purpose of characterizing structure in various types of nonstationary acoustic signals. The computational problem investigated here has direct relevance to the neural coding at the auditory nerve and the more general issue of how to encode complex, time-varying signals with a population of spiking neurons.  相似文献   

10.
With the emergence of location-aware mobile device technologies, communication technologies and GPS systems, the location based queries have attracted great attentions in the database literature. In many user recommendation web services, the spatial preference query is used to suggest the objects based on their spatial proximity with the facilities. In this paper, we study the problem of general spatial skyline (GSSKY) which can provide the minimal candidate set of the optimal solutions for any monotonic distance based spatial preference query. Efficient progressive algorithm called P-GSSKY is proposed to significantly reduce the number of non-promising objects in the computation. Moreover, we also propose spatial join based algorithm, called J-GSSKY, which can compute GSSKY efficiently in terms of I/O cost. The paper conducts a comprehensive performance study of the proposed techniques based on both real and synthetic data.  相似文献   

11.
Efficient algorithms that compute both the estimates and the generalized cross-validation score for the problem of Whittaker-Henderson smoothing are presented. Algorithm efficiency results from carefully exploiting the problem's rich structure to reduce execution time and memory use. The algorithms are much faster than existing ones, and use significantly less memory. MATLAB M-files are included.  相似文献   

12.
The reverse skyline query is very useful in many decision making applications. Given a multi-dimensional dataset P and a query point q, the reverse skyline query returns all the points in P whose dynamic skyline contains q. Although the reverse skyline retrieval has been well-studied in the literature, there is, to the best of our knowledge, no prior work on one of the most intuitive and practical types of reverse skyline queries, namely, group-by reverse skyline (GRS) query, which retrieves the reverse skyline for each group in a specified dataset. We formalize the GRS query including monochromatic and bichromatic versions, and identify its properties, and then propose a set of efficient algorithms for computing the group-by reverse skyline. Extensive experimental evaluation using both real and synthetic datasets demonstrates the performance of our proposed algorithms in terms of effectiveness and efficiency under a variety of experimental settings.  相似文献   

13.
Parallel implementations of Ray Tracing have been enabling real time performance, as the algorithm is embarrassingly parallel. However, in order to achieve both interactivity and real time performance, the algorithm should run at a high frame rates, i.e. at least 60 frames per second. Thus, a custom parallel design in hardware is likely to achieve high rendering performance. In this paper, we improve the GridRT architecture presented in previous work. GridRT is capable of dealing with the main desirable features of Ray Tracing, such as shadows and reflection effects, imposing low area cost and a promising rendering performance. As to this work, an application-specific instruction has been added and the underlaying computation embedded into the processor’s microprogram in order to calculate the ray–triangle intersection computations. These computations are performed in pipeline, whenever possible, yielding to a considerable reduction in terms of cycles per intersection test. The presented architecture is based on the uniform grid acceleration structure. It allows for a massive twofold parallelism: parallel ray–triangle intersection tests as well as parallel processing of many rays. A hardware implementation of the improved architecture is presented, together with the corresponding performance results and resources requirements. The rendering time is reduced by 80% using a grid configuration of eight processing elements and each intersection computation time is reduced by 50% with respect to the original GridRT implementation.  相似文献   

14.
可视性分析广泛应用于GIS的各个方面,视线和视域是其最基本的两个因子。视域的计算方法有很多种,在计算时因为采用的算法和实现算法所运用的编程方法不同,导致效率和精度有很大差异。为了解决由内存受限而无法存储和处理海量区域地理信息数据的问题,提出一种在外部存储器上计算地形视域的高效算法,对该算法进行了分析,并进行了实验验证,表明该算法明显优于现有的其他算法。  相似文献   

15.
The recent availability of detailed geographic data permits terrain applications to process large areas at high resolution. However the required massive data processing presents significant challenges, demanding algorithms optimized for both data movement and computation. One such application is viewshed computation, that is, to determine all the points visible from a given point p. In this paper, we present an efficient algorithm to compute viewsheds on terrain stored in external memory. In the usual case where the observer’s radius of interest is smaller than the terrain size, the algorithm complexity is θ(scan(n 2)) where n 2 is the number of points in an n × n DEM and scan(n 2) is the minimum number of I/O operations required to read n 2 contiguous items from external memory. This is much faster than existing published algorithms.  相似文献   

16.
A general iterative technique for approximate throughput computation of stochastic strongly connected marked graphs is presented. It generalizes a previous technique based on net decomposition through a single input-single output cut, allowing the split of the model through any cut. The approach has two basic foundations. First, a deep understanding of the qualitative behavior of marked graphs leads to a general decomposition technique. Second, after the decomposition phase, an iterative response time approximation method is applied for the computation of the throughput. Experimental results on several examples generally have an error of less than 3%. The state space is usually reduced by more than one order of magnitude; therefore, the analysis of otherwise intractable systems is possible  相似文献   

17.
Over the last few years, the adaptation ability has become an essential characteristic for grid applications due to the fact that it allows applications to face the dynamic and changing nature of grid systems. This adaptive capability is applied within different grid processes such as resource monitoring, resource discovery, or resource selection. In this regard, the present approach provides a self-adaptive ability to grid applications, focusing on enhancing the resources selection process. This contribution proposes an Efficient Resources Selection model to determine the resources that best fit the application requirements. Hence, the model guides applications during their execution without modifying or controlling grid resources. Within the evaluation phase, the experiments were carried out in a real European grid infrastructure. Finally, the results show that not only a self-adaptive ability is provided by the model but also a reduction in the applications’ execution time and an improvement in the successfully completed tasks rate are accomplished.  相似文献   

18.
This paper describes three optimization techniques for the eb3 process algebra. The optimizations are expressed in a new deterministic operational semantics which is shown to be trace-equivalent to a traditional non-deterministic operational semantics. Internal action transitions are eliminated by an efficient preruntime analysis of the structure of a process expression. Execution environments are used to optimize variable instantiation using lazy evaluation. Non-determinism is eliminated by returning a choice between possible transitions. This new operational semantics is implemented in the eb3pai process algebra interpreter to support the eb3 method. The goal of this method is to automate the development of information systems using, among other mechanisms, efficient symbolic computation of process expressions.  相似文献   

19.
A new recursive procedure to compute the Zassenhaus formula up to high order is presented, providing each exponent in the factorization directly as a linear combination of independent commutators and thus containing the minimum number of terms. The recursion can be easily implemented in a symbolic algebra package and requires much less computational effort, both in time and memory resources, than previous algorithms. In addition, by bounding appropriately each term in the recursion, it is possible to get a larger convergence domain of the Zassenhaus formula when it is formulated in a Banach algebra.  相似文献   

20.
Kaiser (1987) described a fast recursive algorithm for computation of equidistant samples of a one-dimensional Gaussian function requiring only two multiplications per sampling point. We show that the algorithm remains valid for two-dimensional Gaussian windows and discuss its application to specific computer vision problems.  相似文献   

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