The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems ha...
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The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes;indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers;rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.
In parallel-distributedsystem,the task distributed to every processing element is imbalance,and so the load of every PE is imbalance,so we introduce load balance *** this paper,by combined mobile agent and prediction...
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In parallel-distributedsystem,the task distributed to every processing element is imbalance,and so the load of every PE is imbalance,so we introduce load balance *** this paper,by combined mobile agent and prediction mathematical model,we propose a simple and effective prediction algorithm of load balancing using in a parallel-distributedsystem,so the resource in system is fully utilized and processing rate of task is improved.
parallel distributed systems in which multiple computers are connected through LAN and WAN are widely used at present. One of the important functions of parallel distributed systems is broadcasting to distribute data ...
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parallel distributed systems in which multiple computers are connected through LAN and WAN are widely used at present. One of the important functions of parallel distributed systems is broadcasting to distribute data over an entire system. Broadcast processing greatly affects the performance of parallel distributed systems, and it is desirable to develop a distribution procedure that completes the processing in a short time. Consequently, there have been many approaches to the minimum broadcast time problem, aiming at the optimal distribution procedure. However, not many past studies of this problem consider a wide-area system in which multiple parallel distributed systems are connected. In this context, this paper considers the minimum broadcast time problem for a wide-area parallel distributed system whose topology can be represented by a certain kind of split graph. It is shown that the problem can be solved in polynomial time in the homogeneous parallel distributed system in which the time required for communication is uniform. In addition, a heuristic algorithm is proposed for a heterogeneous parallel distributed system in which the time required for communication is not uniform. Simulation results reveal that an efficient distribution procedure can be derived in a short time. (C) 2004 Wiley Periodicals, Inc.
In the last few years more and more University Hospitals as well as private hospitals changed to digital information systems for patient record, diagnostic files and digital images. Not only that patient management be...
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ISBN:
(纸本)0819440094
In the last few years more and more University Hospitals as well as private hospitals changed to digital information systems for patient record, diagnostic files and digital images. Not only that patient management becomes easier. it is also very remarkable how clinical research can profit from Picture Archiving and Communication systems (PACS) and diagnostic databases, especially from image databases. Since images are available on the finger tip, difficulties arise when image data needs to be processed, e.g. segmented, classified or co-registered, which usually demands a lot computational power. Today's clinical environment does support PACS very well, but real image processing is still under-developed. The purpose of this paper is to introduce a parallel cluster of standard distributedsystems and its software components and how such a system can be integrated into a hospital environment. To demonstrate the cluster technique we present our clinical experience with the crucial but cost-intensive motion correction of clinical routine and research functional MRI (fMRI) data, as it is processed in our Lab on a daily basis.
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