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Proposal for Neuroinformatics Class (BISC 462)
Dr. Mihail Bota
Fall 2011(2 units)
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
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
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
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:
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 will be determined as following:
Attendance 10%
Project phase 1: 25%
Project phase 2: 25%
Final presentation: 40%