Representing brain activity through statistical model selection in the class of interacting systems of variable memory chains

• Grant number: 14/04801-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: April 01, 2014
• End date: March 31, 2015
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
• Principal Investigator: Jefferson Antonio Galves
• Grantee: Aldana María González Montoro
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat, AP.CEPID

Abstract

Neurons in the brain are organized in huge networks where both physiological and functional connections follow complex patterns. The electrical activity of these systems evolve in time and that evolution depends on the history. During deep sleep, neurons in the cerebral cortex present a highly synchronized global activity. In opposition, in the awake state, these global rhythm disappear giving place to a less synchronized global activity. In experimental conditions, the sleep-like state can be imitated making use of some anesthetics. The transition to the awake-like state can also be reproduced by means of micro-electrical stimulation of the activating ascending areas, originated in nuclei located at the basal forebrain and the brainstem. Thus, it is possible to study the spontaneous association dynamics of neurons during those states and to look for subtle differences between the sleep-like and awake-like states. These issues have been addressed in my PhD dissertation and the results are reported in González-Montoro (2013), González-Montoro et al. (2014) and other two papers that are submitted for evaluation. The general aim of this project is to tackle the problem from a different angle. The new approach is to address the neural association phenomena and to test for different states of the brain using statistical model selection in a new class of stochastic systems recently introduced by members of the NeuroMat team (Galves and Löcherbach 2013).