Computational Neuroscience - Challenges and Implications for Brazilian Education

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.

[1]  Andrew P Davison.  Challenges and solutions in replicability and provenance tracking for simulation projects , 2010, BMC Neuroscience.

[2]  Ian J. Taylor,et al.  Workflows and e-Science: An overview of workflow system features and capabilities , 2009, Future Gener. Comput. Syst..

[3]  Brain Informatics , 2010, Lecture Notes in Computer Science.

[4]  Jules J. Berman,et al.  Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information , 2013 .

[5]  F. Ramus,et al.  Neurogenetics and auditory processing in developmental dyslexia , 2013, Current Opinion in Neurobiology.

[6]  Stian Soiland-Reyes,et al.  PAV ontology: provenance, authoring and versioning , 2013, J. Biomed. Semant..

[7]  Sérgio Manuel Serra da Cruz,et al.  A computational approach for screening dyslexia , 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

[8]  Jonas Kubilius,et al.  A framework for streamlining research workflow in neuroscience and psychology , 2014, Front. Neuroinform..

[9]  Alison Abbott,et al.  Neuroscience: Solving the brain , 2013, Nature.

[10]  Renata Mousinho,et al.  Redes neuronais e transtornos de aprendizagem: rastreio de pessoas com dislexia , 2009 .

[11]  Roberto Lent,et al.  Cem bilhões de neurônios: conceitos fundamentais de neurociência , 2004 .

[12]  Jorge Zavaleta,et al.  Abordagem tecnológica para rastreio de pessoas com dislexia , 2011 .

[13]  Jianhui Chen,et al.  Toward the Data-Brain Driven Systematic Brain Data Analysis , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[14]  Marta Mattoso,et al.  A Foundational Ontology to Support Scientific Experiments , 2012, ONTOBRAS-MOST.

[15]  Tony Hey,et al.  The Fourth Paradigm: Data-Intensive Scientific Discovery , 2009 .

[16]  Henry Markram,et al.  Seven challenges for neuroscience. , 2013, Functional neurology.

[17]  Marta Mattoso,et al.  Towards a Taxonomy of Provenance in Scientific Workflow Management Systems , 2009, 2009 Congress on Services - I.

[18]  Jianhui Chen,et al.  Data-Brain driven systematic human brain data analysis: A case study in numerical inductive reasoning centric investigation , 2012, Cognitive Systems Research.