Analysis of Neural Signals

Studiewijzer 2018/2019

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
• Dimensionality reduction
• 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

• Custom Datacamp course for Python, available at: https://www.datacamp.com/groups/shared_links/f744ffc6a84dd5b7f97fbae36325e7acf2e79a14

Practicummateriaal

• Material will be provided by lecturers

Software

• Matlab, Python

Leerdoelen

Students will learn mathematical foundations and methodologies for the analysis of neural signals, with a focus on computer-assisted analysis of neurophysiological data.

The course has a strong practical component, where the emphasis is on the application of mathematical methods and analytical techniques. Each topic (such as linear algebra, calculus, dynamical systems, Fourier decomposition, spike train analysis) will be covered by a theoretical introduction, followed by a demonstration on real data.

At the end of the course students will be able to:

• 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 program 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 en de student dient zich op deze bijeenkomsten terdege voor te bereiden.

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 program and implement it into Matlab or Python.

Assignment 2

• Students will be asked to develop a Matlab program to analyze neurophysiological data (local field potential 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

Deze cursus draagt bij aan de volgende eindtermen van de opleiding Psychobiologie:

1) Kennis en Inzicht

De bachelor:

• 1e) kan de kennis opgedaan bij een zelfgekozen vak uitleggen.

2) Toepassen Kennis en Inzicht

De bachelor:

• 2b) kan ondersteunende disciplines zoals wis-, natuur- en scheikunde en programmeren toepassen.
• 2g) kan voor de psychobiologie relevante computerprogramma’s en/of programmeertalen gebruiken.
• 2h) kan ruwe data interpreteren en een geschikte (kwantitatieve) analysemethode toepassen.

3) Oordeelsvorming

De bachelor:

• 3e) kan informatie analyseren aan de hand van kwaliteitscriteria en er een eigen oordeel over vormen.

4) Communicatie

De bachelor:

• 4a) kan kennis, bevindingen en standpunten in wetenschappelijk Nederlands en Engels schriftelijk rapporteren en mondeling presenteren.
• 4c) kan op basis van begrip en respect communiceren.

5) Leervaardigheden

De bachelor:

• 5a) kan een zelfstandige en wetenschappelijke werkwijze en houding ontwikkelen.
• 5d) kan een constructieve en synergetische manier van samenwerken ontwikkelen.
• 5e) kan zich in een zelfgekozen deelgebied verdiepen of verbreden.
• 5f) kan zich nieuwe technische vaardigheden eigen maken.
• 5g) kan feedback geven en verwerken.
• 5i) kan reflecteren op eigen gedrag en dit gedrag desgewenst verbeteren.

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

The workload has been reduced (from 3 to 2 assignments). The math part has been restructured: some topics have been simplified (calculus), more time has been given to others (linear algebra). The activities during the laptopcolleges have been reorganized to give students a better indication of what is expected during every day. A new Python component has been added, based on an online learning platform (Datacamp). The effectiveness of this modality will be evaluated at the end of the course.

Contactinformatie

Coördinator

• Umberto Olcese

Docenten

• A.J.P. Heck
• Conrado Bosman
• Paul Mertens