the retention of information over time periods having either fixed or indefinite length. 1 Introduction A major problem in connectionist theory is to develop learning algorithms that can tap the full computational power of neural networks. Much progress has been made with feedforward networks, and attention

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How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched.

The time course of perceptual choice is discussed in a model based on gradual and stochastic accumulation of information in non-linear decision units with leakage (or decay of activation) and competition through lateral inhibition. In special cases, the model becomes equivalent to a classical diffusion process, but leakage and mutual inhibition work together to address several challenges to existing diffusion, random-walk, and accumulator models. The model provides a good account of data from choice tasks using both time-controlled (e.g., deadline or response signal) and standard reaction time paradigms and its overall adequacy compares favorably with that of other approaches. An experimental paradigm that explicitly controls the timing of information supporting different choice alternatives provides further support. The model captures flexible choice behavior regardless of the number of alternatives, accounting for the linear slowing of reaction time as a function of the log of the number of alternatives (Hick’s law) and explains a complex pattern of visual and contextual priming effects in visual word identification. Perceptual Choice 2 When an experience presents itself to the senses, the need often arises to determine its identity or to make some other judgment about it. In experimental paradigms, the time course of this judgment process is

... This paper surveys the past work in solving these problems. The capability of the human visual system with respect to these problems is discussed, too. It is meant to serve as an ultimate goal and a guide for determining recommendations for development of an automatic facial expression analyzer

A major goal of research on networks of neuron-like processing units is to discover efficient learning procedures that allow these networks to construct complex internal representations of their environment. The learning procedures must be capable of modifying the connection strengths in such a way

; and (3) that can lead to profitable experimental programs, testable predictions, and applications to synthetic perception as well as neuroscientific understanding? I believe the answer is yes, and that the use of theoretical neural networks that embody biologically-motivated rules and constraints is a

This paper provides a detailed discussion of wireless resource and channel allocation schemes. We provide a survey of a large number of published papers in the area of fixed, dynamic and hybrid allocation schemes and compare their trade-offs in terms of complexity and performance. We also investigate these channel allocation schemes based on other factors such as distributed/centralized control and adaptability to traffic conditions. Moreover, we provide a detailed discussion on reuse partitioning schemes, effect of hand-offs and prioritization schemes. Finally, we discuss other important issues in resource allocation such as overlay cells, frequency planning, and power control. 1 Introduction Technological advances and rapid development of handheld wireless terminals have facilitated the rapid growth of wireless communications and mobile computing. Taking ergonomics and economics factors into account, and considering the new trends in the telecommunications industry to provide ubiqui...

Abstract:- In this work we survey the Hopfield neural network, introduction of which rekindled interest in the neural networks through the work of Hopfield and others. Hopfield net has many interesting features, applications, and implementations and it comes in two flavors, digital and analog. A

that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive