A brain for a foraging robot

sf_rabams_uva_web An animal survival depends on its ability to find resources in the surrounding environment, in other words in its foraging strategies.

According to Prof. Paul Verschure and his Distributed Adaptive Control theory of mind and brain DAC, when foraging and hoarding, animals behave according to 5 top-level objectives called: “how”, “why”, “what”, “where” and “when” or the so called H4W problem (Verschure, 2012). This form of complex behavior includes: to learn where and when to look for  resources, what to look for, where and when to return to the home base, how  to avoid obstacles and how to act in order to satisfy internal needs.

But how does the brain organization and underlying neural principles account for these complex behaviors?

This is the question that Verschure and his research team SPECS at UPF tried to answer in their latest paper published in Neural Network.

NeuralNetwork DAC-XThe SPECS’ team including lead author G. Maffei, D. Pata, E. Marcos, M. Sanchez and PFMJ. Verschure, have looked at fundamental learning paradigms of classical and operant conditioning and how these are realised by core brain systems such as the cerebellum, hippocampus and neocortex. Biologically constrained models of these systems have been elaborated and the authors have managed for the first time to bring these components together in the model called DAC-X gaining new insights in how the interaction between brain systems is coordinated.

The DAC-X model is not only unique because of its biological detail but also because of its ability to control a robot in real-time facilitating an understanding of the realistic dynamics of brains as they engage with the real world.

NeuralNetwork fig. robotsThe DAC-X robot experiments focus on fundamental behavior o foraging and hoarding. The experiments done using mobile robots show that a naïve agent foraging in a new environment needs to acquire multiple kinds of knowledge, from sensory-motor associations to landmarks and goal oriented strategies. In particular, the agent learns over time to rely on local environmental cues to find useful resources and acquires the navigational planning skills that support efficient decision making, leading to an increase of behavioural efficiency, observed in terms of the overall cost-reward ratio.

The DAC-X model shows in detail how concurrently core brain systems contribute to this complex task: the hypothalamus defining the dominant needs, the cerebellum shaping specific action patterns to negotiate the environment, the hippocampus performing internal simulations of potential routes with the prefrontal cortex setting the specific behavioral goals. This work sheds light on the synergies between multiple learning systems in the brain and how these could lead to coherent behavioural outcomes observed in rodents, and other mammals. The objective of DAC-X and the whole DAC series of models is to render whole brain models that can assist us in understanding the brain in health and disease giving rise to novel control methods for robots and better diagnostics and interventions in the clinic.

izquierda-derecha-maffei-alex-escuredo-pata-verschure-upf-1450541544453The SPECS group has already made the first steps in this direction by the DAC grounded approach to stroke rehabilitation called the Rehabilitation Gaming System RGS.

see also  http://www.elperiodico.com/es/noticias/sociedad/investigadores-pompeu-fabra-desarrollan-robot-que-comporta-como-rata-4765003


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