Enseñanza de los Modelos de Computación Conexionista

Presentamos en esta ponencia la experiencia docente llevada a cabo en la asignatura de «Introducción a los Modelos de Computación Conexionista» durante los últimos tres años en el Centro Superior de Informática de la Universidad de La Laguna

Automatic Prognostic Determination and Evolution of Cognitive Decline using Artificial Neural Networks

Abstract. This work tries to go a step further in the development of methods based on automatic learning techniques to parse and interpret data relating to cognitive decline (CD). There have been studied the neuropsychological tests of 267 consultations made over 30 patients by the Alzheimer's Patient Association of Gran Canaria in 2005. The Sanger neural network adaptation for missing values treatment has allowed making a Principal Components Analysis (PCA) on the successfully obtained data. The results show that the first three obtained principal components are able to extract information relating to functional, cognitive and instrumental sintomatology, respectively, from the test. By means of these techniques, it is possible to develop tools that allow physicians to quantify, view and make a better pursuit of the sintomatology associated to the cognitive decline processes, contributing to a better knowledge of these ones

Modeling the implications of nitric oxide dynamics on information transmission: An automata networks approach

Nitric oxide (NO) is already recognized as an important signaling molecule in the brain. It diffuses easily and the nervous cell's membrane is permeable to NO. The information transmission is three-dimensional, which is different from synaptic transmission. NO operates in two different ways: Close and specific at the synapses of neurons, and as a volumetric transmitter sending signals to various targets, regardless of their anatomy, connectivity or function, when multiple nearby sources act simultaneously. These modes of operation seem to be the basis by which NO is involved in many central mechanisms of the brain, such as learning, memory formation, brain development and synaptogenesis. This work focuses on the effect of NO dynamics on the environment through which it diffuses, using automata networks. We study their implications in the formation of complex functional structures in the volume transmission (VT), which are necessary for the synchronous functional recruitment of neuronal populations. We qualitatively and quantitatively analyze the proposed model regarding these characteristics through the concepts of entropy and mutual information. The proposed deterministic model allows the incorporation of fuzzy dynamics. With that, a generalized model based on fuzzy automata networks can be provided. This allows the generation and diffusion processes of NO to be arbitrarily produced and maintained over time. This model can accommodate arbitrary processes in decision-making mechanisms and can be part of a complete formal VT framework in the brain and artificial neural networks