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The link between individual glial cells and synaptic transmission

Grant number: 23/15585-6
Support Opportunities:Scholarships abroad - Research Internship - Scientific Initiation
Effective date (Start): April 15, 2024
Effective date (End): August 14, 2024
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
Principal Investigator:Ricardo Felipe Ferreira
Grantee:João Pedro Pirola
Supervisor: Rodrigo Felipe de Oliveira Pena
Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Research place: Florida Atlantic University, United States  
Associated to the scholarship:23/09094-0 - Identification of glial cells from artificial neural networks, BP.IC

Abstract

A prominent and essential element in the brain, contributing to several cognitive functions, are glial cells. Identifying glial cell signatures in neuronal spiking and how these signatures change in the course of a pathology represents a promising target for therapeutic interventions. Understanding the extent of their involvement in brain functions may reveal potential opportunities for using glial cells during the rehabilitation of unhealthy personnel, offering a promising avenue for enhancing recovery and cognitive rehabilitation efforts. In this sense, spike-train cross-correlation functions provide a measure of synaptic communication and we will use them to identify how glial cells shape identifiable signatures while spike transmission takes place. Thus, it is plausible to explore the signatures in the presence of different types of glial cells not only in health and pathological states but also in terms of how much influence there is in neuronal communication and up to the learning and decision-making process. In this work, we want to identify the link between individual glial cells and synaptic transmission through supervised training of artificial neural networks. The results will be compared with those obtained with other machine learning classifiers.

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