Neuronal effective connectivity

• Grant number: 22/10524-6
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: November 01, 2022
• End date: October 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
• Principal Investigator: Ricardo Felipe Ferreira
• Grantee: João Victor Russo Izzi
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

Abstract

Records of neuronal activity reveal that electrical spikes may occur spontaneously and irregularly and undergo variations even when the neuron is exposed to the same stimuli. These empirical observations suggest a probabilistic framework for the mathematical description and treatment of neural phenomena. In this sense, the evolution of neuronal activity over time can be modeled by a stochastic process. Identifying the connections that define the neural circuit is essential to explain how such representations are produced and to predict how the network will behave in new situations. The objective of this research project is the development of theoretical and applied research with respect to the statistical and probabilistic aspects of neuronal connectivity. In particular, we are interested in the study of the effective connectivity between neurons, taking into account that the stochastic behavior of neurons has an influence on the dynamics of the neural network. At first, the student will study descriptive measures to detect the existence and measure the magnitude of interactions between neural structures. Then, he must study causality measures to detect the direction and signal of the information flow between neurons, that is, study how to infer the effective connectivity between them. Finally, the student will perform, from a simulation study, both the comparison of the performance of the descriptive measures in the detection of neuronal connections, as the comparison of the performance of the descriptive measures of causality. An application in real data will also be provided in this study.