Catálogo de publicaciones - libros

A universal approximator, such as multilayer perceptron, is a tool that allows mapping of any multidimensional continuous function. The aim of this paper is to discuss a method of perceptron training that would result in its ability to map the functions constituting the solutions of partial differential equations of first and second order. The developed algorithm has been validated by means of equations whose analytical solutions are known.

Capturing regularities in high-dimensional data is an important problem in machine learning and signal processing. Here we present a statistical model that learns a nonlinear representation from the data that reflects abstract, invariant properties of the signal without making requirements about the kind of signal that can be processed. The model has a hierarchy of two layers, with the first layer broadly corresponding to Independent Component Analysis (ICA) and a second layer to represent higher order structure. We estimate the model using the mathematical framework of Score Matching (SM), a novel method for the estimation of non-normalized statistical models. The model incorporates a squaring nonlinearity, which we propose to be suitable for forming a higher-order code of invariances. Additionally the squaring can be viewed as modelling subspaces to capture residual dependencies, which linear models cannot capture.

We propose a new nonparametric test for component independence which is based on application of data compressors to ranked data. For two-component data sample the idea is to break the sample in two parts and permute one of the components in the second part, while leaving the first part intact. The resulting two samples are then jointly ranked and a data compressor is applied to the resulting (binary) data string. The components are deemed independent if the string cannot be compressed. This procedure gives a provably valid test against all possible alternatives (that is, the test is distribution-free) provided the data compressor was ideal.

In this paper, the -pages graph layout problem is solved by a new neural model. This model consists of two neural networks performing jointly in order to minimize the same energy function. The neural technique applied to this problem allows to reduce the energy function by changing outputs from both networks –outputs of first network representing location of nodes in the nodes line, while the outputs of the second one meaning the page where the edges are drawn.

A detailed description of the model is presented, and the technique to minimize an energy function is fully described. It has proved to be a very competitive and efficient algorithm, in terms of quality of solutions and computational time, when compared to the state-of-the-art heuristic methods specifically designed for this problem. Some simulation results are presented in this paper, to show the comparative efficiency of the methods.

Treatment of general structured information by neural networks is an emerging research topic. Here we show how representations for graphs preserving all the information can be devised by Recursive Principal Components Analysis learning. These representations are derived from eigenanalysis of extended vectorial representations of the input graphs. Experimental results performed on a set of chemical compounds represented as undirected graphs show the feasibility and effectiveness of the proposed approach.

We propose a method to estimate the graph structure from data for a Markov random field (MRF) model. The method is valuable in many practical situations where the true topology is uncertain. First the similarities of the MRF variables are estimated by applying methods from information theory. Then, employing multidimensional scaling on the dissimilarity matrix obtained leads to a 2D topology estimate of the system. Finally, applying uniform thresholding on the node distances in the topology estimate gives the neighbourhood relations of the variables, hence defining the MRF graph estimate. Conditional independence properties of a MRF model are defined by the graph topology estimate thus enabling the estimation of the MRF model parameters e.g. through the pseudolikelihood estimation scheme. The proposed method is demonstrated by identifying MRF model for a telecommunications network, which can be used e.g. in analysing the effects of stochastic disturbances to the network state.

In an attempt to bridge the gap between appraisal theory and the neuroscience of emotions, we have created a computational neural model in which a discrepancy between the internal value of global self-esteem and a more temporary, stimulus-inspired current self-esteem initiates an ongiong emotional response. We assign possible neural correlates to the nodes in this model, amongst which the orbitofrontal cortex and cingulate gyrus. We propose disruptions of the model analogous to states of depression.

In this paper, we will address the endeavors of three disciplines, Psychology, Neuroscience, and Artificial Neural Network (ANN) modeling, in explaining how the mind perceives and attends information. More precisely, we will shed some light on the efforts to understand the allocation of attentional resources to the processing of emotional stimuli. This review aims at informing the three disciplines about converging points of their research and to provide a starting point for discussion.

Over the last few years, functional Magnetic Resonance Imaging (fMRI) has emerged as a new and powerful method to map the cognitive states of a human subject to specific functional areas of the subject brain. Although fMRI has been widely used to determine average activation in different brain regions, the problem of automatically decoding the cognitive state from instantaneous brain activations has received little attention. In this paper, we study this prediction problem on a complex time-series dataset that relates fMRI data (brain images) with the corresponding cognitive states of the subjects while watching three 20 minute movies. This work describes the process we used to reduce the extremely high-dimensional feature space and a comparison of the models used for prediction. To solve the prediction task we explored a standard linear model frequently used by neuroscientists, as well as a model, that now are the state-of-art in this area. Finally, we provide experimental evidence that non-linear models such as and especially are significantly better.

Attention and emotion are closely interlinked and recent results have shown some of the neuro-physiological details of the effects of attention on emotion through the distractor devaluation (DD) effect. We develop a possible neural attention control architecture to explain the DD effect, and show by specific simulation how the N2pc (an early component of attention movement) can be encoded to produce encoding of devaluation of distractors.