Authors:
Raimundo José Macário Costa
1
;
Luís Alfredo Vidal de Carvalho
2
;
Emilio Sánchez Miguel
3
;
Renata Mousinho
2
;
Renato Cerceau
4
;
Lizete Pontes Macário Costa
5
;
Jorge Zavaleta
2
;
Laci Mary Barbosa Manhães
2
and
Sérgio Manuel Serra da Cruz
6
Affiliations:
1
Universidade Federal Rural do Rio de Janeiro - UFRRJ, Brazil
;
2
Universidade Federal do Rio de Janeiro – UFRJ, Brazil
;
3
Salamanca University (U.S.A.L), Spain
;
4
Universidade Federal do Rio de Janeiro – UFRJ and National Regulatory Agency for Private Health Insurance and Plans (ANS), Brazil
;
5
Rio de Janeiro State University (UERJ), Brazil
;
6
Universidade Federal Rural do Rio de Janeiro - UFRRJ, Programa de Educação Tutorial (PET-SI/UFRRJ) and Programa de Pós- Graduação em Modelagem Matemática e Computacional (PPGMMC/UFRRJ), Brazil
Keyword(s):
Education, Neuroscience, Computer Science, Databases, Artificial Intelligence, Cognitive Science.
Related
Ontology
Subjects/Areas/Topics:
Classroom Management
;
Computer-Supported Education
;
Information Technologies Supporting Learning
;
Learning/Teaching Methodologies and Assessment
;
Metrics and Performance Measurement
;
Social Context and Learning Environments
;
Standards and Interoperability
;
Teacher Evaluation
;
Ubiquitous Learning
Abstract:
Understanding the core function of the brain is one the major challenges of our times. In the areas of
neuroscience and education, several new studies try to correlate the learning difficulties faced by children
and youth with behavioral and social problems. This work aims to present the challenges and opportunities
of computational neuroscience research, with the aim of detecting people with learning disorders. We
present a line of investigation based on the key areas: neuroscience, cognitive sciences and computer
science, which considers young people between nine and eighteen years of age, with or without a learning
disorder. The adoption of neural networks reveals consistency in dealing with pattern recognition problems
and they are shown to be effective for early detection in patients with these disorders. We argue that
computational neuroscience can be used for identifying and analyzing young Brazilian people with several
cognitive disorders.