Neural Control of Actions Involving Different Coordinate Systems

di Parm

Post-training discriminative pruning for RBMs

One of the major challenges in the area of artificial neural networks is the identification of a suitable architecture for a specific problem. Choosing an unsuitable topology can exponentially increase the training cost, and even hinder network convergence. On the other hand, recent research indicates that larger or deeper nets can map the problem features into a more appropriate space, and thereby improve the classification process, thus leading to an apparent dichotomy. In this regard, it is interesting to inquire whether independent measures, such as mutual information, could provide a clue to finding the most discriminative neurons in a network. In the present work we explore this question in the context of Restricted Boltzmann Machines, by employing different measures to realize post-training pruning. The neurons which are determined by each measure to be the most discriminative, are combined and a classifier is applied to the ensuing network to determine its usefulness. We find that two measures in particular seem to be good indicators of the most discriminative neurons, producing savings of generally more than 50% of the neurons, while maintaining an acceptable error rate. Further, it is borne out that starting with a larger network architecture and then pruning is more advantageous than using a smaller network to begin with. Finally, a quantitative index is introduced which can provide information on choosing a suitable pruned network.Fil: Sánchez Gutiérrez, Máximo. Universidad Autónoma Metropolitana; MéxicoFil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Close, John Goddard. Universidad Autónoma Metropolitana; Méxic

Analysis of coding principles in the olfactory system and their application in cheminformatics

Unser Geruchssinn vermittelt uns die Wahrnehmung der chemischen Welt. Im Laufe der Evolution haben sich in unserem olfaktorischen System Mechanismen entwickelt, die wahrscheinlich optimal auf die Erfüllung dieser Aufgabe angepasst sind. Die Analyse dieser Verarbeitungsstrategien verspricht Einblicke in effiziente Algorithmen für die Kodierung und Verarbeitung chemischer Information, deren Entwicklung und Anwendung dem Kern der Chemieinformatik entspricht. In dieser Arbeit nähern wir uns der Entschlüsselung dieser Mechanismen durch die rechnerische Modellierung von funktionellen Einheiten des olfaktorischen Systems. Hierbei verfolgten wir einen interdisziplinären Ansatz, der die Gebiete der Chemie, der Neurobiologie und des maschinellen Lernens mit einbezieht

A dynamic neural field approach to the covert and overt deployment of spatial attention

International audienceAbstract The visual exploration of a scene involves the in- terplay of several competing processes (for example to se- lect the next saccade or to keep fixation) and the integration of bottom-up (e.g. contrast) and top-down information (the target of a visual search task). Identifying the neural mech- anisms involved in these processes and in the integration of these information remains a challenging question. Visual attention refers to all these processes, both when the eyes remain fixed (covert attention) and when they are moving (overt attention). Popular computational models of visual attention consider that the visual information remains fixed when attention is deployed while the primates are executing around three saccadic eye movements per second, changing abruptly this information. We present in this paper a model relying on neural fields, a paradigm for distributed, asyn- chronous and numerical computations and show that covert and overt attention can emerge from such a substratum. We identify and propose a possible interaction of four elemen- tary mechanisms for selecting the next locus of attention, memorizing the previously attended locations, anticipating the consequences of eye movements and integrating bottom- up and top-down information in order to perform a visual search task with saccadic eye movements

Integration of sensorimotor mappings by making use of redundancies

Hemion N, Joublin F, Rohlfing K. Integration of sensorimotor mappings by making use of redundancies. In: IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers, eds. The 2012 International Joint Conference on Neural Networks (IJCNN). Brisbane, Australia: IEEE; 2012

Betweenness and Diversity in Journal Citation Networks as Measures of Interdisciplinarity -- A Tribute to Eugene Garfield --

Journals were central to Eugene Garfield's research interests. Among other things, journals are considered as units of analysis for bibliographic databases such as the Web of Science (WoS) and Scopus. In addition to disciplinary classifications of journals, journal citation patterns span networks across boundaries to variable extents. Using betweenness centrality (BC) and diversity, we elaborate on the question of how to distinguish and rank journals in terms of interdisciplinarity. Interdisciplinarity, however, is difficult to operationalize in the absence of an operational definition of disciplines, the diversity of a unit of analysis is sample-dependent. BC can be considered as a measure of multi-disciplinarity. Diversity of co-citation in a citing document has been considered as an indicator of knowledge integration, but an author can also generate trans-disciplinary--that is, non-disciplined--variation by citing sources from other disciplines. Diversity in the bibliographic coupling among citing documents can analogously be considered as diffusion of knowledge across disciplines. Because the citation networks in the cited direction reflect both structure and variation, diversity in this direction is perhaps the best available measure of interdisciplinarity at the journal level. Furthermore, diversity is based on a summation and can therefore be decomposed, differences among (sub)sets can be tested for statistical significance. In an appendix, a general-purpose routine for measuring diversity in networks is provided