El diseño de la innovación

La creciente prominencia del diseño ha incrementado el interés por nuevos métodos y estrategias para lograr la innovación, tanto en el mundo profesional como en la enseñanza del diseño. Este artículo presenta tres enfoques diferentes para diseñar innovación: la práctica de la creatividad a través del compromiso con áreas del conocimiento ajenas al del diseño, la fertilización cruzada interdisciplinaria entre diferentes campos del diseño y un enfoque narrativo al diseño

Dissenyant la innovació

La prominència creixent del disseny ha fet que cada vegada es presti més atenció als nous mètodes i estratègies per aconseguir innovació, tant en el món professional com en l'ensenyament del disseny. Aquest article s'ocupa de tres enfocaments diferents de la innovació en el disseny; la fertilització creuada interdisciplinària entre diferents camps del disseny; i una aproximació narrativa al disseny

Oral History Conversation with Rasto Ivanic (GroupSolver)

Interview with the co-founder and CEO of Groupsolver, Rasto Ivanic. In the interview he answers questions about his early life and how he became involved in this social venture. He discusses some of the trials and tribulations associated with Social Entrepreneurship. Groupsolver is a service that collects feedback from customers/constituents and supplies it to the relevant party

Bump formation in a binary attractor neural network

This paper investigates the conditions for the formation of local bumps in the activity of binary attractor neural networks with spatially dependent connectivity. We show that these formations are observed when asymmetry between the activity during the retrieval and learning is imposed. Analytical approximation for the order parameters is derived. The corresponding phase diagram shows a relatively large and stable region, where this effect is observed, although the critical storage and the information capacities drastically decrease inside that region. We demonstrate that the stability of the network, when starting from the bump formation, is larger than the stability when starting even from the whole pattern. Finally, we show a very good agreement between the analytical results and the simulations performed for different topologies of the network.Comment: about 14 page

Improved storage capacity of hebbian learning attractor neural network with bump formations

The final publication is available at Springer via http://dx.doi.org/10.1007/11840817_25Proceedings of 16th International Conference on Artificial Neural Networks, Athens, Greece, September 10-14, 2006, Part IRecently, bump formations in attractor neural networks with distance dependent connectivities has become of increasing interest for investigation in the field of biological and computational neuroscience. Although the distance dependent connectivity is common in biological networks, a common fault of these network is the sharp drop of the number of patterns p that can remembered, when the activity changes from global to bump-like, than effectively makes these networks low effective. In this paper we represent a bump-based recursive network specially designed in order to increase its capacity, which is comparable with that of randomly connected sparse network. To this aim, we have tested a selection of 700 natural images on a network with N = 64K neurons with connectivity per neuron C. We have shown that the capacity of the network is of order of C, that is in accordance with the capacity of highly diluted network. Preserving the number of connections per neuron, a non-trivial behavior with the radius of the connectivity has been observed. Our results show that the decrement of the capacity of the bumpy network can be avoided.The authors acknowledge the financial support from the Spanish Grants DGI.M. CyT. FIS2005-1729, Plan de Promoción de la Investigación UNED and TIN 2004–07676-G01-01.We also thank David Dominguez for the fruitful discussion of the manuscript