Wikipedia has become a key educational resource, and NeuroMat is a leading institution in Brazil in disseminating science through this electronic encyclopedia. Giulia Gamba, Revista Cásper, 01/2017. (In Portuguese.)
We consider the problem of estimating the context tree of a stationary ergodic process with finite alphabet without imposing additional conditions on the process. As a starting point we introduce a Hamming metric in the space of irreducible context trees and we use the properties of the weak topology in the space of ergodic stationary processes to prove that if the Hamming metric is unbounded, there exist no consistent estimators for the context tree. Even in the bounded case we show that there exist no two-sided confidence bounds. However we prove that one-sided inference is possible in this general setting and we construct a consistent estimator that is a lower bound for the context tree of the process with an explicit formula for the coverage probability. We develop an efficient algorithm to compute the lower bound and we apply the method to test a linguistic hypothesis about the context tree of codified written texts in European Portuguese.
Background: Neuroevolution comprises the use of evolutionary computation to define the architecture and/or to train artificial neural networks (ANNs). This strategy has been employed to investigate the behavior of rats in the elevated plus-maze, which is a widely used tool for studying anxiety in mice and rats. New method: Here we propose a neuroevolutionary model, in which both the weights and the architecture of artificial neural networks (our virtual rats) are evolved by a genetic algorithm. Comparison with Existing Methods: This model is an improvement of a previous model that involves the evolution of just the weights of the ANN by the genetic algorithm. In order to compare both models, we analyzed traditional measures of anxiety behavior, like the time spent and the number of entries in both open and closed arms of the maze. Results: When compared to real rat data, our findings suggest that the results from the model introduced here are statistically better than those from other models in the literature. Conclusions: In this way, the neuroevolution of architecture is clearly important for the development of the virtual rats. Moreover, this technique allowed the comprehension of the importance of different sensory units and different number of hidden neurons (performing as memory) in the ANNs (virtual rats).
A NeuroMat initiative, the Latin American School on Computational Neuroscience (LASCON) is one of the most important computational neuroscience schools in the world. Its mission is to provide an intensive training for advanced undergraduate and graduate students, an introduction to mathematical and computational tools for neural modeling and analysis. The school relies on theory-driven sections and practical, hands-on activities.
During the last NeuroMat workshop, PI Antonio Carlos Roque presented a project on simulating neuronal behaviors associated to the Galves-Löcherbach model. Computational simulation could provide insights on brain dynamics, especially with working with very large sample sizes.