We propose an extension to Description Logics (DLs) with uncertainty which unifies and/or generalizes a number of existing frameworks for DLs with uncertainty. To this end, we first give a classification of these fram...
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We propose an extension to Description Logics (DLs) with uncertainty which unifies and/or generalizes a number of existing frameworks for DLs with uncertainty. To this end, we first give a classification of these frameworks and identify the essential features as well as proper ties of the various combination functions allowed in the underlying uncer tainty formalisms they model. This also allows us express the semantics of the DL elements in a flexible manner. We illustrate how various DLs with uncertainty can be expressed in our generic framework.
Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron, and synaptic depression when the presynaptic neuron fires shortly after. The dependence of...
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ISBN:
(纸本)0262195348
Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron, and synaptic depression when the presynaptic neuron fires shortly after. The dependence of synaptic modulation on the precise timing of the two action potentials is known as spike-timing dependent plasticity or STDP. We derive STDP from a simple computational principle: synapses adapt so as to minimize the postsynaptic neuron's variability to a given presynaptic input, causing the neuron's output to become more reliable in the face of noise. Using an entropy-minimization objective function and the biophysically realistic spike-response model of Gerstner (2001), we simulate neurophysiological experiments and obtain the characteristic STDP curve along with other phenomena including the reduction in synaptic plasticity as synaptic efficacy increases. We compare our account to other efforts to derive STDP from computational principles, and argue that our account provides the most comprehensive coverage of the phenomena. Thus, reliability of neural response in the face of noise may be a key goal of cortical adaptation.
Uncertainty reasoning has been a challenging issue in AI and database research. During the last two decades, numerous frameworks have been proposed by extending the standard logic programming and deductive database sy...
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Home service robots have a wide range of potential applications, such as home security, patient caring, cleaning, etc. The services provided by the robots in each application area are being defined as markets are form...
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Many large-scale scientific experiments and simulations generate very large amounts of data (on the order of terabytes to petabytes), spanning thousands of files and data sets. We have developed a software system, cal...
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Today’s scientific simulations often generate huge amounts of data for data archival, data analysis, and visualization. These data are stored in high-performance distributed storages that consist of a network-oriente...
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in this paper we consider polynomial kernel representations for behaviors. For behaviors over fields it is well-known that minimal representations, i.e. representations with minimal row degrees, are exactly those repr...
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The intricacy of real-time data service management increases mainly due to the emergence of applications operating in open and unpredictable environments, increases in software complexity, and need for performance gua...
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Concurrent accesses to a file frequently occur in a distributed computing environment where a few number of network-attached servers are designated as a data storage pool and the clients are physically connected to th...
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