Statistics for Neuroscience
(Neuroscience 9506)
The goal of the seminar is to provide students with the opportunity to gain a deeper understanding of the logic behind inferential statistics, and to learn a common base of standard multivariate statistical techniques. The course is not particularly oriented towards the arithmetic calculations underlying statistical procedures, rather we will focus on gaining an understanding of the logic behind various parametric and non-parametric statistical techniques common in the neurosciences. There will be a practical aspect to the course, namely learning to use R for statistical computation and graphical display of data.
The course is different than Computational Neuroscience I: Data Analysis, which is focused on using Matlab for data processing and analysis.
Typical topics covered in the course include: logic of statistics & experimental design; t-tests; the General Linear Model; type-I error & post-hoc tests; Analysis of Variance (ANOVA); Analysis of Co-Variance (ANCOVA); Multivariate ANOVA (MANOVA); correlation & regression; Multiple regression; model benchmarking; Chi-Square; non-parametric statistics; statistical issues for fMRI analysis
click [here] for the course website
This course will be offered Jan-April 2010
Fridays, 9:30am – 12:30pm, RRI 4th floor conference room
*** note: our first class (Fri Jan 15th) will be in RRI 5260a
Computational Neuroscience 1: Data Analysis
(Neuroscience 9519)
The goal of this one-semester graduate course is to provide students with a basic set of skills using a number of different data analysis techniques that are commonly used in Neuroscience research. Students will build up a “toolbox” of useful computational techniques that they can use in their own research using Matlab, which will be used throughout the course. No previous experience in Matlab or in programming is required.
click [here] for the course website
This course will be offered Sep-Dec 2009
Fridays, 9:30am – 12:30pm, RRI 4th floor conference room
Computational Neuroscience II: Neural Models
(Neuroscience 9520)
The goal of this one-semester graduate course is to provide students with broad knowledge of computational models of neural systems from neuron to network, hands-on experience using Matlab to implement and test models, and the ability to critically assess original research articles in which computational modeling techniques are used to address current issues in Neuroscience research.
This course was last offered Sept-Dec, 2008
