Analysis of Neural Signals

Studiewijzer 2019/2020

Globale inhoud

The main subjects covered in the course will be:

• Mathematical methods for signal analysis:
  • Linear algebra
• Calculus:
  • Advanced calculus of real functions
  • Functions of several variables
• Introduction to dynamical systems:
  • Systems of linear differential equations
• Programming in MATLAB:
  • From algorithms to programs
  • Performing signal processing and statistics in MATLAB
• Introduction to programming in Python
• Spectral analysis:
  • Theory and practice
  • Spectral filtering
  • Introduction to image processing
• Analysis of neuronal spiking data:
  • Spike sorting
  • Analysis of single neuron and population activity
• Introduction to artificial neural networks for classification
• Foundations of information theory, encoding/decoding

Studiemateriaal

Literatuur

• Material provided by lecturer

• A short compendium of most of the topics covered during the course can be found in the following book: Wallish et al., Matlab for Neuroscientists (Second Edition), Elsevier

• The cloud environment SOWISO (https://uva.sowiso.nl/) will be used by students to learn, practice, and assess the mathematical methods and techniques taught in this course

Practicummateriaal

• Material will be provided by lecturers

Software

• Matlab, Python

Leerdoelen

• interpret and apply the main concepts in linear algebra;
• interpret and apply methods and techniques of advanced calculus;
• formulate, carry out, and write down mathematical computations in a correct way;
• compute the behavior of systems of linear differential equations;
• read, interpret and design computer programs in MATLAB and Python;
• describe and use the most relevant signal processing techniques for spectral analysis and for the analysis of spiking data;
• select the most appropriate signal processing techniques for spectral analysis and for the analysis of spiking data;
• independently develop MATLAB programs to perform simple signal processing routines.

Onderwijsvormen

• Hoorcollege
• Laptopcollege
• Zelfstudie
• Zelfstandig werken aan bijv. project/scriptie

Verdeling leeractiviteiten

Aanwezigheid

Aanwezigheidseisen opleiding (OER-B):

• Deelname aan alle practica, computerpractica, veldwerk en werkcolleges in het curriculum is verplicht. Eventueel aanvullende eisen worden per onderdeel in de studiewijzer omschreven. Hier staat ook beschreven wat de eventuele consequenties zijn van het niet nakomen van deze verplichting.

Aanvullende eisen voor dit vak:

Participation to hoorcolleges and laptopcolleges is compulsory. Students may miss at most 10% of all activities, and any absence needs to be communicated to the course coordinator.

Toetsing

Grading matrices/rubrics and criteria for each of the graded component will be uploaded on Canvas at the beginning of the course.

Assignments will be done in groups of 2/3 students, and will lead to a single group grade. Assignments will be primarily based on the development of MATLAB code. Groups for assignments will be formed at the beginning of the course. About one week after the deadline of an assignment, feedback will be provided by the teaching assistants. Late submissions will not be accepted.

The hertentamen will combine topics covered in the deeltoets and tentamen.

Opdrachten

Assignment 1

• Students will be asked to design a computer program (focused on applications of linear algebra) and implement it into Matlab

Assignment 2

• Students will be asked to develop a Matlab program to analyze neurophysiological data (local field potentials and spiking activity)

Fraude en plagiaat

Dit vak hanteert de algemene 'Fraude- en plagiaatregeling' van de UvA. Hier wordt nauwkeurig op gecontroleerd. Bij verdenking van fraude of plagiaat wordt de examencommissie van de opleiding ingeschakeld. Zie de Fraude- en plagiaatregeling van de UvA: http://student.uva.nl

Weekplanning

Rooster

Het rooster van dit vak is in te zien op DataNose.

Eindtermen

Via de Zichtbare Leerlijnen Creator kun je zien aan welke eindtermen de leerdoelen van deze cursus bijdragen en hoe de  vakleerdoelen, leerlijndoelen en eindtermen van de opleiding aan elkaar gekoppeld zijn:

https://datanose.nl/#program[BSc%20PB]/outcomes 

https://datanose.nl/#program[BSc%20PB]/trajectories

Aanvullende informatie

Knowledge in basic mathematics and statistics is required (trigonometry, differential calculus, complex numbers, probability theory, main statistical tests).

Students are required to have followed at least an introductory course on mathematics (Basis Wiskunde or similar), and on programming in Matlab (e.g. Inleiding Programmeren or similar).

Capacity: Max. 50 students

Verwerking vakevaluaties

A formal introduction to Python has been included. Assignment 1 is now focused on the application of linear algebra concepts to the investigation of biological phenomena. More emphasis is given to applications (relevant for Psychobiologists) of the math topics covered in the course.

Contactinformatie

Coördinator

• Umberto Olcese

Docenten

Lectures

• A.J.P. Heck
• Conrado Bosman

Teaching assistants

• Paul Mertens
• Joao Patriota
• Wessel van der Ham