Learning objectives
1. Knowledge and understanding of concepts. Students should know and understand the theoretical principles and basic mathematical tools that recur daily in neural data analysis.
2. Autonomy of application of concepts. Students should be able to independently apply the acquired theoretical and technical knowledge to the analysis of real data in MATLAB or other language of choice.
3. Problem Analysis. Students should be able to identify, for the specific experimental question, which experimental and analysis techniques are most suitable and efficient to answer it.
4. Communication skills. Students should master the ability to present and discuss orally some analysis techniques applied on experimental data.
Prerequisites
Basic knowledge of neurophysiology and elements of statistics.
Course unit content
The course aims to provide students with basic skills in neural data analysis. Basic concepts of statistics, linear algebra, signal analysis, encoding/decoding will be covered, supported by continuous application examples on different types of neural data (single neuron, LFP, EEG, ...). The student will be guided in mastering the mathematical principles underlying data analysis with examples in MATLAB, and the application of these principles to analyze experimental data.
Full programme
Introduction of elements of linear algebra, matrix calculus, vector spaces. Brief review of basic statistical concepts and mathematical analysis. Signal analysis: signal-to-noise ratio, Fourier transform, filters, wavelets, spectrograms. Analysis of neuronal spike trains, population analysis, neural state space. Dimension reduction techniques, clustering. Encoding and decoding models, classification and regression. Hints of neural networks.
Bibliography
1. Case Studies in Neural Data Analysis. A Guide for the Practicing Neuroscientist. The MIT Press, 2016.
2. MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB. 2nd Edition, Academic Press, 2014.
Teaching methods
The various topics will be covered mainly through classroom lectures (writing small portions of code requires the use of a laptop). In addition, students will be offered to perform analysis of a neural dataset on a topic of their choice in order to familiarize themselves with the analysis techniques covered in class. The teacher is available outside of class time to discuss any critical issues.
Assessment methods and criteria
The final examination consists of a two-part oral test. In the first part, the student will present the results of the analysis on the chosen dataset. This will be followed by a discussion of the results presented, extended to touch on key concepts covered in the course.
Other information
2030 agenda goals for sustainable development
