Computational Neuroscience

Academic year: 2010-2011 Course code module 1MBMW-K-010 Semester: 1st semester Credits: 6 Study load (hours) 168 Theory (hours): 30,00 Practice/Exercises(hours): 15,00 Other (hours): Part-time program: Instructor(s) Michele Giugliano Erik De Schutter Language of instruction: English Semester exam information: exam in the 1st semester Contract restriction information: 1. Prerequisites *Algemene competenties Basic mathematics and calculus; strong interests in interdisciplinary topics; basic cellular biology; *Relation to other courses Knowledge of ‘Cellular and Molecular Neuroscience’ (1M BMW) and ‘Systems Neuroscience’ (1M BMW) is a plus but is not compulsory. *Sequentiality System Neuroscience (1MBMW-K-008) 2. Objectives (expected learning outcomes) In this course, we introduce the students to a selection of topics, in search for the mechanistic dissection on how the mammalian nervous system work. Particular emphasis is placed in introducing the language of cellular electrophysiology and biophysics, as the great discoveries were made in these domains where the availability of solid theoretical and computational tools revealed to be imperative.  3. Course content Similar to physics and engineering, modern Neurosciences reached a level of maturity where theoretical analysis and mathematical modeling are standard tools, complementing experimental methods. From molecular investigations, to cellular-, microcircuit-, network-, and system-level understanding of the brain (dys)functions, computational neuroscience aims at bridging the multifaceted features of these levels of organization and at providing an interdisciplinary common language. Identifying unifying concepts and common principles are among the ultimate goals of this discipline. Simulating complex experiments, testing the consequences of hypotheses, and performing manipulations that would be impossible to carry out in real experiments with today methods, further represent one of the most successful contribution of computational neurosciences, towards a quantitative understanding of the concerted working of molecules, cells, networks and systems.   4. Teaching method Direct contact: Lectures Exercise sessions Seminars (possible question and answer sessions) Personal work: Case studies - in group Excursion(s) 5. Assessment method Exam: Oral, with written preparation Continuous assessment: Assignments 6. Compulsory reading – study material Research and study material will be given or indicated by the lecturers during the course. 7. Recommended reading - study material Bear, Connor, Paradiso's for the biological introduction (chapter 1-4)  Abbott LF, Dayan P (2001) Theoretical Neuroscience. Cambridge, MA: MIT Press. Hertz, J., Krogh, A., & Palmer, R. G. (1991). Introduction to the theory of neural computation. Reading, MA: Addison-Wesley. 8. Tutoring Prof. Michele Giugliano is available for further questions laatste aanpassing: last update: 28/01/2010 13:25 jan.vos