Catálogo de publicaciones - libros

A new line of investigation known as quantum neurology has been born in recent years. One of its objectives is to accomplish a better explanation of psychism. It basically explains the unity of consciousness, its holistic character, and the indeterminism of its responses. How is this “phenomenological ” explained in classical neurological architecture? After commenting on the properties of classical architecture, we focus on the proposal of Edelman, since we consider it as probably one of the better proposals explaining psychism. The discussion of Edelman’s proposal, from the viewpoint of the problem about the “physical support” of psychism in classical physics, allows us to evaluate the strengths of his proposal, as well as the remaining insufficiencies in his explanation. The “heuristic” way of quantum neurology offers a new approach to the “phenomenological ” that does not contradict, but completes classical architecture. The discussion regarding the Hameroff-Penrose hypothesis allows us to propose that the psycho-bio-physical ontology would have an architecture with three levels (or sub-architectures) and two (or three) interface systems among them. This hypothetical architecture permits us to reflect on the production of ontologies, architectures, and functional logics (real and artificial). In any case, the new quantum neurology would suggest new formulations of the psycho-bio-physical ontology by means of the graph theory (classical neurology) and of topology (quantum neurology).

The new Quantum Information Theory augurs powerful machines that obey the “entangled” logic of the subatomic world. Parallelism, entanglement, teleportation, no-cloning and quantum cryptography are typical peculiarities of this novel way of understanding computation. In this article, we highlight and explain these fundamental ingredients that make Quantum Computing potentially powerful.

Theories of how the brain computes can be differentiated in three general conceptions: the algorithmic approach, the neural information processing (neurocomputational) approach and the dynamical systems approach. The discussion of key features of brain organization (i.e. structure with function) demonstrates the self-organizing character of brain processes at the various spatio-temporal scales. It is argued that the features associated with the brain are in support of its description in terms of dynamical systems theory, and of a concept of computation to be developed further within this framework.

In this work, we first present a view of the philosophic study of Intelligent Behavior in a wide sense. We expose some key ideas to understand Intelligence and Rationality in an operational way based on the notions of Prediction and Randomness. In particular, we hypothesize that unpredictability is the key concept of Intelligence while not randomness is the key concept of Rationality. Next we undertake the study of Emotional Behavior discussing the basic principle of emotional attachment which is modeled by means of an operational definition of the Self. We hypothesize that the most basic principles of the Emotional Behavior emerges from a sort of ego-centric mechanism.

This work aims to describe the application of a novel machine consciousness model to a particular problem of unknown environment exploration. This relatively simple problem is analyzed from the point of view of the possible benefits that cognitive capabilities like attention, environment awareness and emotional learning can offer. The model we have developed integrates these concepts into a situated agent control framework, whose first version is being tested in an advanced robotics simulator. The implementation of the relationships and synergies between the different cognitive functionalities of consciousness in the domain of autonomous robotics is also discussed.

We present an insect-inspired approach to orientation estimation for panoramic images. It has been shown by Zeil et al. (2003) that relative rotation can be estimated from global image differences, which could be used by insects and robots as a visual compass [1]. However the performance decreases gradually with the distance of the recording positions of the images. We show that an active vision approach based on local translational movements can significantly improve the orientation estimation. Tests were performed with a mobile robot equipped with a panoramic imaging system in a large entrance hall. Our approach is minimalistic insofar as it is solely based on image differences.

Social robots are receiving much interest in the robotics community. The most important goal for such robots lies in their interaction capabilities. This work describes the robotic head CASIMIRO, designed and built with the aim of achieving interactions as natural as possible. CASIMIRO is a robot face with 11 degrees of freedom. Currently, the robot has audio-visual attention (based on omnidirectional vision and sound localization abilities), face detection, head gesture recognition, owner detection, etc. The results of interviews with people that interacted with the robot support the idea that the robot has relatively natural communication abilities, although certain aspects should be further developed.

Recent experiments show that inhibitory interneurons are coupled by electrical synapses. In this paper the information transmission properties of a network of three interneurons, coupled by electrical synapses alone, are studied by means of numerical simulations. It is shown that the network is capable to transfer the information contained in its presynapstic inputs when they are near synchronous: i.e. the network behaves as a coincidence detector. Thus, it is hypothesized that this property hold in general for networks of larger size. Lastly it is shown that these findings agree with recent experimental data.

Traditional interpretation of early visual image processing in a retinal level has focused exclusively on spatial aspects of receptive fields (RFs). We have learned recently that RFs are spatiotemporal entities and this characterization is crucial in understanding and modelling circuits in early visual processing ([1]). We present a generalization of the layered computation concept to describe visual information processing in the joint space-time domain.

Measurements of some visual functions (visual fields, acuity and visual inversion) versus intensity of stimulus, including facilitation, carried out by Justo Gonzalo in patients with central syndrome, are seen to follow Stevens’ power law of perception. The characteristics of this syndrome, which reveals aspects of the cerebral dynamics, allow us to conjecture that Stevens’ law is in these cases a manifestation of the universal allometric scaling power law associated with biological neural networks. An extension of this result is pointed out.