Time: Monday and Wednesday 2:55-4:10pm
Location: M Van Rensselar Hall G73
Mondays, 5pm - 7pm in Comstock Hall B160.
Instructor: Christiane Linster, NBB firstname.lastname@example.org; 2544331. Office hours: after lectures and by appointment.
TA: Shane Peace , NBB, email@example.com.
Office hours: Mondays 10-11 am in Comstock Hall B160
Course webpages: http://courses.cit.cornell.edu/bionb330
Course syllabus and materials
Lab syllabus and materials
ANNOUNCEMENT: Meeting times for 4-credit students:
Wednesday, September 19th 10 am (W245 Mudd), Friday, September 21st 3pm (W245 Mudd) - COLOR Vision assignment
Wednesday, September 26th 10 am (W245 Mudd) and Friday, September 28th 3 pm (W245 Mudd) - Contrast assignment
What is Computational Neuroscience? and what does this course attempt to do?
* Attempts to understand how brains "compute"
* Using computer to simulate and model brain function
* Applying techniques from computational fields (math, physics) to study brain function
The main objective of this course is to present some major concepts in the field of computational neuroscience and to give the students some idea of common approaches taken by computational neuroscientists. The basic thinking in the presentation of the field given here is that the key contributions of computational neuroscience are conceptual, and do not rely on a deep understanding of the underlying mathematics, but rather on an understanding of "systems neuroscience". However, some mathematical concepts will be presented, because in my opinion, some of the major insights gained from computational neuroscience to understanding the nervous system result from translating biological facts into mathematical concepts and vice-versa.
If at any point during this class, your understanding of the mathematics presented is less than rock-solid, don't worry. I will not expect that students taking this class can pass an exam in linear algebra of differential equations. I will only expect you to understand what the mathematical concepts represent, not to be able to do the math yourselves. In the same spirit, if you don't understand some of the readings, don't worry! Readings partially consist of Journal papers, which are dense and difficult. I will expect you to make an effort, but will not expect you to grasp everything you read without help and explanations. The readings are necessary in order to help the students to get a feeling for the field, but I am well aware of the fact that papers are rarely written clearly enough to be easily understood by a student in a 300-level class.
The most important thing to remember is: ask questions! If you do not feel comfortable asking questions in class, email me or come talk to me. The only means at the disposition of an instructor to know how well the material is understood by the class is feedback from the students! I am more than willing to repeat anything that is difficult, to explain concepts and to spend time with students. However, I can only do that if you let me know when difficulties arise!
Interesting books on the subject include:
The computational brain by P.S. Churchland and T.J. Sejnowski, MIT Press.
Theoretical Neuroscience by P. Dayan and L.F. Abbott, MIT Press
The Journal of Computational Neuroscience, Kluwer Academic Publishers
Neural Computations, MIT Press