Theories of efficient sensory processing have considered the regularities of image properties due to the structure of the environment in order to explain properties of neuronal representations of the visual world. The...
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Theories of efficient sensory processing have considered the regularities of image properties due to the structure of the environment in order to explain properties of neuronal representations of the visual world. The regularities imposed on the input to the visual system due to the regularities of the active selection process mediated by the voluntary movements of the.:yes have been considered to a much lesser degree. This is surprising, given that the active nature of vision is well established. The present article investigates statistics of image features at the center of gaze of human subjects navigating through a virtual environment and avoiding and approaching different objects. The analysis shows that contrast can be significantly higher or lower at fixation location compared to random locations, depending on whether subjects avoid or approach targets. Similarly, significant differences in the distribution of responses of model simple and complex cells between horizontal and vertical orientations are found over timescales of tens of seconds. B, clustering the model simple cell responses, it is established that gaze was directed toward three distinct features of intermediate complexity the vast majority of time. Thus, this study demonstrates and quantifies how the visuomotor tasks of approaching and avoiding objects during navigation determine feature statistics of the input to the visual system through the combined influence on body and eye movements.
The aim of this paper is to provide new insights about the circuitry and the role of the dorsal column nuclei (DCN) in processing somatosensory information. The presence of glycinergic cells, a second type of DCN inte...
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The aim of this paper is to provide new insights about the circuitry and the role of the dorsal column nuclei (DCN) in processing somatosensory information. The presence of glycinergic cells, a second type of DCN interneurons in addition to well-known GABAergic interneurons, opens the door to more complex interactions between cuneate cells as well as to a new hypothesis about the computational implications of such interactions. The research posed here fits in a broader context in the field of the sensory systems and deals with the general issue on the role of subcortical structures (i.e the thalamus) in processing sensory information. (C) 2004 Elsevier B.V. All rights reserved.
A new technique for optimally encoding a given source statistical properties of which are described by the first-order model is introduced. The calculation of codeword lengths is based on construction of a new source ...
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ISBN:
(纸本)0769521088
A new technique for optimally encoding a given source statistical properties of which are described by the first-order model is introduced. The calculation of codeword lengths is based on construction of a new source with statistics that is determined by the consecutive redistribution of the probabilities of symbols in accordance with their original probabilities at each stage of the encoding. The proposed method performs equally well for different orders of symbol probabilities. While codewords are generated by a, separate combinatorial procedure, the overall computational cost of the proposed method is lower than that for the Huffman code.
We investigate questions, relating to optimal binary coding of a language, for which a probability distribution is given on its words (for a stochastic language). As optimal we understand the coding, which gives minim...
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We investigate questions, relating to optimal binary coding of a language, for which a probability distribution is given on its words (for a stochastic language). As optimal we understand the coding, which gives minimum of mathematical expectation of a length of the coded word, or minimum of the coding cost. For any stochastic language with a finite value of the entropy we establish lower and upper bounds of the optimal coding cost, dependent only on the entropy, and we prove their unimprovability. For any stochastic context-free language with unique derivation it is found necessary and sufficient condition of existence of finite values of the optimal coding cost and the entropy. Also an effective method of calculation of the entropy is found for the case when the considered condition holds.
In this paper we discuss convergence properties for genetic algorithms. By looking at the effect of mutation on convergence, we show that by running the genetic algorithm for a sufficiently long time we can guarantee ...
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In this paper we discuss convergence properties for genetic algorithms. By looking at the effect of mutation on convergence, we show that by running the genetic algorithm for a sufficiently long time we can guarantee convergence to a global optimum with any specified level of confidence. We obtain an upper bound for the number of iterations necessary to ensure this, which improves previous results. Our upper bound decreases as the population size increases. We produce examples to show that in some cases this upper bound is asymptotically optimal for large population sizes. The final section discusses implications of these results for optimal coding of genetic algorithms.
One knows from the Algorithmic Complexity Theory' [2-5, 8, 14] that a word is incompressible on average. For words of pattern x(m), it is natural to believe that providing x and m is an optimal average representat...
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One knows from the Algorithmic Complexity Theory' [2-5, 8, 14] that a word is incompressible on average. For words of pattern x(m), it is natural to believe that providing x and m is an optimal average representation. On the contrary, for words like x circle plus y (i.e., the bit to bit x or between x and y), providing x and y is not an optimal description on average. In this work, we sketch a theory of average optimal representation that formalizes natural ideas and operates where intuition does not suffice. First, we formulate a definition of K-optimality on average for a pattern, then demonstrate results that corroborate intuitive ideas, and give worthy insights into the best compression in more complex cases. (C) 1998-Elsevier Science B.V. All rights reserved.
In survival models, when the factor of interest is a continuous variable or is expressed through a group of several variables, the classical measures of risk, i. e. relative risk and odds ratio, are not appropriate an...
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In survival models, when the factor of interest is a continuous variable or is expressed through a group of several variables, the classical measures of risk, i. e. relative risk and odds ratio, are not appropriate and there is no standard measure of dependence between survival and the considered factor. The Information Gain has been proposed by Linfoot (1957) and Kent (1983), giving any parametric model as a generalization of the squared product-moment correlation coefficient of the linear regression model with normal errors. By using simulation methods, we studied the statistical properties of the information gain as a measure of dependence, in the particular case of survival regression models. We suggest several efficient applications of this informational concept to some classical problems of regression analysis and prognostic analysis. Our ideas are illustrated through an example on the prognosis of idiopathic dilated cardiomyopathy.
We discuss the capacity of the Gaussian channel with feedback. In general it is not easy to give an explicit formula for the capacity of a Gaussian channel, unless the channel is without feedback or a white Gaussian c...
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We discuss the capacity of the Gaussian channel with feedback. In general it is not easy to give an explicit formula for the capacity of a Gaussian channel, unless the channel is without feedback or a white Gaussian channel. We consider the case where a constraint, given in terms of the covariance functions of the input processes, is imposed on the input processes. It is shown that the capacity of the Gaussian channel can be achieved by transmitting a Gaussian message and using additive linear feedback.
For the Gaussian channel Y(t) = Φ(ξ(s), Y(s);s ≦ t) + X(t), the mutual information I(ξ, Y) between the message ξ(·) and the output Y(·) is evaluated, where X(·) is a Gaussian noise. Furthermore, th...
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