A dynamic neural field approach to natural and efficient human-robot collaboration

A major challenge in modern robotics is the design of autonomous robots that are able to cooperate with people in their daily tasks in a human-like way. We address the challenge of natural human-robot interactions by using the theoretical framework of dynamic neural fields (DNFs) to develop processing architectures that are based on neuro-cognitive mechanisms supporting human joint action. By explaining the emergence of self-stabilized activity in neuronal populations, dynamic field theory provides a systematic way to endow a robot with crucial cognitive functions such as working memory, prediction and decision making . The DNF architecture for joint action is organized as a large scale network of reciprocally connected neuronal populations that encode in their firing patterns specific motor behaviors, action goals, contextual cues and shared task knowledge. Ultimately, it implements a context-dependent mapping from observed actions of the human onto adequate complementary behaviors that takes into account the inferred goal of the co-actor. We present results of flexible and fluent human-robot cooperation in a task in which the team has to assemble a toy object from its components.The present research was conducted in the context of the fp6-IST2 EU-IP Project JAST (proj. nr. 003747) and partly financed by the FCT grants POCI/V.5/A0119/2005 and CONC-REEQ/17/2001. We would like to thank Luis Louro, Emanuel Sousa, Flora Ferreira, Eliana Costa e Silva, Rui Silva and Toni Machado for their assistance during the robotic experiment

The lifecycle of powerful AGN outflows

During the course of this conference, much evidence was presented that points to an intimate connection between the energetic outflows driven by AGN and the energy budget and quite possibly also the evolution of their gaseous environments. However, it is still not clear if and how the AGN activity is triggered by the cooling gas, how long the activity lasts for and how these effects give rise to the observed distribution of morphologies of the outflows. In this contribution we concentrate on the high radio luminosity end of the AGN population. While most of the heating of the environmental gas may be due to less luminous and energetic outflows, these more powerful objects have a very profound influence on their surroundings. We will describe a simple model for powerful radio galaxies and radio-loud quasars that explains the dichotomy of their large-scale radio morphologies as well as their radio luminosity function.Comment: 6 pages, contribution to 'Heating vs. coooling in galaxies and galaxy clusters', Garching 2006, proceedings to be published by Springer (ESO Astrophysics Symposia), eds. H. Boehringer, P. Schuecker, G.W. Pratt & A. Finogueno

Numerical simulation scheme of one-and two-dimensional neural fields involving space-dependent delays

International audienceNeural Fields describe the spatio-temporal dynamics of neural populations involving spatial axonal connections between neurons. These neuronal connections are delayed due to the finite axonal transmission speeds along the fibers inducing a distance-dependent delay between two spatial locations. The numerical simulation in 1-dimensional neural fields is numerically demanding but may be performed in a reasonable run time by implementing standard numerical techniques. However 2-dimensional neural fields demand a more sophisticated numerical technique to simulate solutions in a reasonable time. The work presented shows a recently developed numerical iteration scheme that allows to speed up standard implementations by a factor 10-20. Applications to some pattern forming systems illustrate the power of the technique

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Dimension reduction in heterogeneous neural networks: Generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA)

We propose, and illustrate via a neural network example, two different approaches to coarse-graining large heterogeneous networks. Both approaches are inspired from, and use tools developed in, methods for uncertainty quantification (UQ) in systems with multiple uncertain parameters – in our case, the parameters are heterogeneously distributed on the network nodes. The approach shows promise in accelerating large scale network simulations as well as coarse-grained fixed point, periodic solution computation and stability analysis. We also demonstrate that the approach can successfully deal with structural as well as intrinsic heterogeneities