A Theory of Information Processing for Adaptive Systems: Inspiration from Biology, Formal Analysis and Application to Artificial Systems: a Dissertation Submitted in Partial Fulfillment of the Requirements for the Doctoral Degree

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: Junio 200

Local and global gating of synaptic plasticity

Mechanisms influencing learning in neural networks are usually investigated on either a local or a global scale. The former relates to synaptic processes, the latter to unspecific modulatory systems. Here we study the interaction of a local learning rule that evaluates coincidences of pre- and postsynaptic action potentials and a global modulatory mechanism, such as the action of the basal forebrain onto cortical neurons. The simulations demonstrate that the interaction of these mechanisms leads to a learning rule supporting fast learning rates, stability, and flexibility. Furthermore, the simulations generate two experimentally testable predictions on the dependence of backpropagating action potential on basal forebrain activity and the relative timing of the activity of inhibitory and excitatory neurons in the neocortex.We are grateful to Konrad Körding and Mike Merzenich for valuable discussions of the previous work on the learning rule and the experimental data and Daniel Kiper for comments on a previous version of the manuscript. We are happy to acknowledge the support of SPP Neuroinformatics (grants 5002–44888/2&3 to P. F. M. J. V.), SNF (grant 31-51059.97, awarded to P. K.), and an FPU grant from MEC (M. A. S.-M., Spain)

Towards a more realistic evaluation: Testing the ability to predict future tastes of matrix factorization-based recommenders

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in RecSys '11 Proceedings of the fifth ACM conference on Recommender systems, http://dx.doi.org/10.1145/2043932.2043990.The use of temporal dynamic terms in Matrix Factorization (MF) models of recommendation have been proposed as a means to obtain better accuracy in rating prediction task. However, the way such models have been tested may not be a realistic setting for recommendation. In this paper, we evaluated rating prediction and top-N recommendation tasks using a MF model with and without temporal dynamic terms under two evaluation settings. Our experiments show that the addition of dynamic parameters do not necessarily yield to better results on these tasks when a more strict time-aware separation of train/test data is performed, and moreover, results may vary notably when different evaluation schemes are used.This work is supported by the Spanish Government (TIN 2008-06566-C04-02) and by the Comunidad de Madrid and Universidad Autónoma de Madrid (CCG10-UAM/TIC-5877)

The effect of low number of points in clustering validation via the negentropy increment

This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 74, 16, (2011) DOI: 10.1016/j.neucom.2011.03.023Selected papers of the 10th International Work-Conference on Artificial Neural Networks (IWANN2009)We recently introduced the negentropy increment, a validity index for crisp clustering that quantifies the average normality of the clustering partitions using the negentropy. This index can satisfactorily deal with clusters with heterogeneous orientations, scales and densities. One of the main advantages of the index is the simplicity of its calculation, which only requires the computation of the log-determinants of the covariance matrices and the prior probabilities of each cluster. The negentropy increment provides validation results which are in general better than those from other classic cluster validity indices. However, when the number of data points in a partition region is small, the quality in the estimation of the log-determinant of the covariance matrix can be very poor. This affects the proper quantification of the index and therefore the quality of the clustering, so additional requirements such as limitations on the minimum number of points in each region are needed. Although this kind of constraints can provide good results, they need to be adjusted depending on parameters such as the dimension of the data space. In this article we investigate how the estimation of the negentropy increment of a clustering partition is affected by the presence of regions with small number of points. We find that the error in this estimation depends on the number of points in each region, but not on the scale or orientation of their distribution, and show how to correct this error in order to obtain an unbiased estimator of the negentropy increment. We also quantify the amount of uncertainty in the estimation. As we show, both for 2D synthetic problems and multidimensional real benchmark problems, these results can be used to validate clustering partitions with a substantial improvement.This work has been funded by DGUI-CAM/UAM (Project CCG10-UAM/TIC-5864

Semantic Construction and Form: Foundations of the Communicative Dimension in Contemporary Architecture

Aquest article investiga les característiques de la concepció simbòlica de l’arquitectura al panorama que sorgeix de la crisi de la modernitat i evoluciona fins als nostres dies, així com els aspectes més significatius en relació amb la seva comprensió semàntica. Per això aborda els conceptes relacionats amb el signe i utilitza la semiòtica com a metodologia original d’anàlisi amb l’objectiu d’interpretar els elements visuals que transmeten un significat. En aquest sentit, les eines semiòtiques com a símbol, metàfora o icona, entre d’altres, interpreten l’expressió de formes arquitectòniques i formulen la seva comprensió posterior i les converteixen en eines de comunicació. Aquesta visió de l’arquitectura contemporània situa el seu llenguatge en el context de la teoria dels signes, contribuint a explicar, amb una nova metodologia, les emocions que transmet un objecte arquitectònic i la seva relació amb lintèrpret (usuari).This paper examines the characteristics of the symbolic conception of architecture in the panorama that arises from the crisis of modernity and evolves to the present day, as well as the most significant aspects in relation to its semantic understanding. To this end, the research addresses the concepts related to the sign and uses Semiotics as an original analysis methodology with the aim of interpreting the visual elements that convey a meaning. Therefore, semiotic tools such as symbol, metaphor, or icon, among others, interpret the expression of architectural forms and formulate their subsequent understanding, turning them into communication tools. This approach of contemporary architecture takes place in the context of the theory of signs, helping to explain, with a new methodology, the emotions transmitted by an architectural object and its relationship with the interpreter (user).Este artículo investiga las características de la concepción simbólica de la arquitectura en el panorama que surge de la crisis de la modernidad y evoluciona hasta nuestros días, así como los aspectos más significativos en relación con su comprensión semántica. Para ello aborda los conceptos relacionados con el signo y utiliza la Semiótica como metodología original de análisis con el objetivo de interpretar los elementos visuales que transmiten un significado. En este sentido, las herramientas semióticas como símbolo, metáfora o icono, entre otras, interpretan la expresión de formas arquitectónicas y formulan su comprensión posterior convirtiéndolas en herramientas de comunicación. Esta visión de la arquitectura contemporánea sitúa su lenguaje en el contexto de la teoría de los signos, contribuyendo a explicar, con una nueva metodología, las emociones que transmite un objeto arquitectónico y su relación con el intérprete (usuario).Peer Reviewe

Optimal classification of Gaussian processes in homo- and heteroscedastic settings

A procedure to derive optimal discrimination rules is formulated for binary functional classification problems in which the instances available for induction are characterized by random trajectories sampled from different Gaussian processes, depending on the class label. Specifically, these optimal rules are derived as the asymptotic form of the quadratic discriminant for the discretely monitored trajectories in the limit that the set of monitoring points becomes dense in the interval on which the processes are defined. The main goal of this work is to provide a detailed analysis of such optimal rules in the dense monitoring limit, with a particular focus on elucidating the mechanisms by which near-perfect classification arises. In the general case, the quadratic discriminant includes terms that are singular in this limit. If such singularities do not cancel out, one obtains near-perfect classification, which means that the error approaches zero asymptotically, for infinite sample sizes. This singular limit is a consequence of the orthogonality of the probability measures associated with the stochastic processes from which the trajectories are sampled. As a further novel result of this analysis, we formulate rules to determine whether two Gaussian processes are equivalent or mutually singular (orthogonal)The research has been supported by the Spanish Ministry of Economy, Industry, and Competitiveness—State Research Agency, Projects MTM2016-78751-P and TIN2016-76406-P(AEI/FEDER, UE), and Comunidad Autónoma de Madrid, Project S2017/BMD-368

Semantic Construction and Form: Foundations of the Communicative Dimension in Contemporary Architecture

Este artículo investiga las características de la concepción simbólica de la arquitectura en el panorama que surge de la crisis de la modernidad y evoluciona hasta nuestros días, así como los aspectos más significativos en relación con su comprensión semántica. Para ello aborda los conceptos relacionados con el signo y utiliza la Semiótica como metodología original de análisis con el objetivo de interpretar los elementos visuales que transmiten un significado. En este sentido, las herramientas semióticas como símbolo, metáfora o icono, entre otras, interpretan la expresión de formas arquitectónicas y formulan su comprensión posterior convirtiéndolas en herramientas de comunicación. Esta visión de la arquitectura contemporánea sitúa su lenguaje en el contexto de la teoría de los signos, contribuyendo a explicar, con una nueva metodología, las emociones que transmite un objeto arquitectónico y su relación con el intérprete (usuario).This paper examines the characteristics of the symbolic conception of architecture in the panorama that arises from the crisis of modernity and evolves to the present day, as well as the most significant aspects in relation to its semantic understanding. To this end, the research addresses the concepts related to the sign and uses Semiotics as an original analysis methodology with the aim of interpreting the visual elements that convey a meaning. Therefore, semiotic tools such as symbol, metaphor, or icon, among others, interpret the expression of architectural forms and formulate their subsequent understanding, turning them into communication tools. This approach of contemporary architecture takes place in the context of the theory of signs, helping to explain, with a new methodology, the emotions transmitted by an architectural object and its relationship with the interpreter (user)

Evaluation of negentropy-based cluster validation techniques in problems with increasing dimensionality

The aim of a crisp cluster validity index is to quantify the quality of a given data partition. It allows to select the best partition out of a set of potential ones, and to determine the number of clusters. Recently, negentropy-based cluster validation has been introduced. This new approach seems to perform better than other state of the art techniques, and its computation is quite simple. However, like many other cluster validation approaches, it presents problems when some partition regions have a small number of points. Different heuristics have been proposed to cope with this problem. In this article we systematically analyze the performance of different negentropy-based validation approaches, including a new heuristic, in clustering problems of increasing dimensionality, and compare them to reference criteria such as AIC and BIC. Our results on synthetic data suggest that the newly proposed negentropy-based validation strategy can outperform AIC and BIC when the ratio of the number of points to the dimension is not high, which is a very common situation in most real applications.The authors thank the financial support from DGUI-CAM/UAM (Project CCG10-UAM/TIC-5864

Fast response and coherent oscillations in small-world Hodgkin-Huxley neural networks

This is an electronic version of the paper presented at the I Jornadas Técnicas de la ETS de Informática, held in Madrid on 200

An autonomous robot that learns approach-avoidance behaviors: lessons from the brain to the robot

This is an electronic version of the paper presented at the I Jornadas Técnicas de la ETS de Informática, held in Madrid on 200