Nicolás Navarro

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Knowledge of mammalian tooth formation is increasing, through numerous genetic and developmental studies. The prevalence of teeth in fossil remains has led to an intensive description of evolutionary patterns within and among lineages based on tooth morphology. The extent to which developmental processes have influenced tooth morphologies and therefore the(More)
In the Arctic, food limitation is one of the driving factors behind small mammal population fluctuations. Active throughout the year, voles and lemmings (arvicoline rodents) are central prey in arctic food webs. Snow cover, however, makes the estimation of their winter diet challenging. We analyzed the isotopic composition of ever-growing incisors from(More)
In this paper we describe a fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. Takagi-Sugeno-Kang (TSK) fuzzy logic is used to motivate a small mobile robot to acquire complex behaviors and to perform environment recognition. This method is implemented and tested in behavior based(More)
In this paper we investigate and develop a real-world reinforcement learning approach to autonomously recharge a humanoid Nao robot [1]. Using a supervised reinforcement learning approach, combined with a Gaussian distributed states activation, we are able to teach the robot to navigate towards a docking station, and thus extend the duration of autonomy of(More)
The evolution of mammalian dentition is constrained by functional necessity and by the non-independence of morphological structures. Efficient chewing implies coherent tooth coordination from development to motion, involving covariation patterns (integration) within dental parts. Using geometric morphometrics, we investigate the modular organization of the(More)
Reinforcement learning (RL) is a biologically supported learning paradigm, which allows an agent to learn through experience acquired by interaction with its environment. Its potential to learn complex action sequences has been proven for a variety of problems, such as navigation tasks. However, the interactive randomized exploration of the state space,(More)
In this work, we present a neurocomputational model for auditory-cue fear acquisition. Computational fear conditioning has experienced a growing interest over the last few years, on the one hand, because it is a robust and quick learning paradigm that can contribute to the development of more versatile robots, and on the other hand, because it can help in(More)
In this paper we investigate real-time adaptive extensions of our fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. The main idea is to introduce active battery level sensors and recharge zones to improve robot behavior for reaching survivability in environment exploration. In order to(More)
We describe the application of high-resolution 3D microcomputed tomography, together with 3D landmarks and geometric morphometrics, to validate and further improve previous quantitative genetic studies that reported QTL responsible for variation in the mandible shape of laboratory mice using a new backcross between C57BL/6J and A/J inbred strains. Despite(More)
1. Abstract Classical fear conditioning has experienced a growing interest over the last decade. Fear learning mechanisms are a simple and robust learning paradigm that involves sensory and motor areas. We believe that a deeper study of these mechanisms will contribute not only to a better understanding of fear conditioning but also to the development of(More)