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Proposal for Neuroinformatics Class (BISC 462) Dr. Mihail Bota Fall 2011(2 units) Presentation This course emphasizes the need for neuroinformatics in neuroscience and provides an overview of the most important topics and challenges. The course will introduce neuroscientists to the relevant computer science techniques and approaches and vice versa. Neuroinformatics aims to store neuroscience data in computer-readable formats and infer from these structure-function relations of different parts of the nervous system. This is accomplished by applying principles, methods and algorithms from mathematics and artificial intelligence. A critical challenge of neuroinformatics is the computer representation of data and metadata specific to certain neuroscience fields at different organization levels of the nervous system. Examples include gene expression patterns and neuron types identified in different brain regions, connections between brain regions, axonal projections of neuron types and classes, as well as metadata regarding employed techniques and experiments. The course will include projects in which students from both neuroscience and computer science work in teams at a project split in three parts over the semester. The aim of this project is to teach students how to design, develop and populate simple neuroinformatics applications that can be either used in constructing more sophisticated systems, or implemented as useful tools in other research laboratories. Emphasis will be given to real-world examples and approaches. The course will therefore include presentation of the most important publicly available neuroinformatics systems. Readings will be provided in advance and it will include materials that will complement and detail the presented topics. There are no specific prerequisites but knowledge of database design and of web accessible applications will be a plus. Proposed syllabus: 1. What is Neuroinformatics. Setting goals for the course. General principles of neuroanatomy. (08/25) 2. Data and metadata types in neurosciences. Elements of database design. Representation of neuroscience data and metadata in database formats. (09/01) 3. Introduction in mapping of neuroanatomical data (I): gene expression data, and neurons. Neuroinformatic databases for gene expression data and developmental databases. (09/08). 4. Introduction in cytoarchitecture and cytology. Neuroinformatic databases for neurons and neural components: Senselab and CoCoDat (09/15) 5. Project phase I presentation and discussion. Introduction in mapping of neuroanatomical data (II): brain regions, and fiber tracts. (09/22) 6. Introduction in brain regions and major fiber tracts of the mammalian central nervous system. Neuroinformatic databases for brain regions and neuroanatomical connections: CoCoMac and Temporal Lobe. (09/29) 7. Introduction in mapping of neuroanatomical data (III): manual and semiautomatic neuroanatomy data translation. (10/06) 8. Data mining: Principles and main techniques. (10/13) 9. Neuroinformatic databases for brain regions and neuroanatomical connections: BAMS.(10/20) 10. Neuroinformatics systems for literature and experimental data management. Neuroscholar. (10/27) 11. Project phase II presentation and discussion. Neural models repositories: Brain Operating Principles Database (BODB), Senselab.(11/03) 12. Brain imaging databases. Allen Brain Institute Databases, and Nesys. BrainMap and Brede databases. (11/10) 13. Vocabularies and ontologies in neuroinformatics. (11/17) 14. Thanksgiving Recess. 15. Final Project Presentation (12/01). Grading Grading will be determined as following: 1. 2. 3. 4. Attendance 10% Project phase 1: 25% Project phase 2: 25% Final presentation: 40%