Analysis of Neural Signals

12 EC

Semester 1, periode 1

5102ANS12Y

Eigenaar Bachelor Psychobiologie
Coördinator Umberto Olcese
Onderdeel van Bachelor Psychobiologie, jaar 3

Studiewijzer 2017/2018

Globale inhoud

The main subjects covered in the course will be:

  • Mathematical methods for signal analysis:Linear algebra
  • Advanced calculus of real functions
  • Calculus of functions of several variables
  • Systems of linear differential equations
  • Programming in MATLAB:Performing signal processing and statistics in MATLAB
  • Spectral analysis:theory and practice
  • Spectral filtering
  • Introduction to image processing
  • Analysis of neuronal spiking dataSpike 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

Practicummateriaal

  • Material will be provided by lecturers

Software

  • Matlab

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;
  • 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

Activiteit

Aantal uur

Hoorcollege

60

Laptopcollege (supervised + independent)

128
Tentamen 6
Vragenuur 4
Zelfstudie 138
Totaal 28*12 EC 336

  
Weeks 1-3, 5-7:

Hoorcolleges (2 hours per day), Computerpractica (own laptop is required, 2 hours per day, with assistance) and self-study (rooms will be available on most days day for 2 additional hours). During computerpractica, students will work individually on exercises that help get the skills that are needed for the (graded) group assignments. Assignments will be prepared during computerpractica and during self study.

Weeks 4, 8:

Self study (preparation for final test, completion of assignments).

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:

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

Onderdeel en weging Details Opmerkingen

Eindcijfer

50%

Tentamen

Moet ≥ 5 zijn, HerkansbaarOpen questions

50%

Deeltoets

Open questions

50%

Final exam

Open questions

50%

Assignments

Moet ≥ 5 zijn

25%

Assignment 1

25%

Assignment 2

50%

Assignment 3

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

Assignments will be done in groups of 3/4 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 tentamenThe hertentamen will combine topics covered in the deeltoets and tentamen

Opdrachten

Assignment 1

  • Students will be asked to design an algorithm and implement it into Matlab.

Assignment 2

  • Students will be asked to solve a mathematical problem in Matlab

Assignment 3

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

Onderstaande opdrachten komen aan bod in deze cursus:

  •    Naam opdracht 1 : beschrijving 2
  •    Naam opdracht 2 : beschrijving 1
  •    ....

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: www.uva.nl/plagiaat

Weekplanning

 

Week Day Hoorcollege topic / other activity Hoorcollege lecturer Laptopcollege activities Notes
1 Mon Introduction Olcese Exercises + Assignment 1  
  Tue Programming 1 Olcese Exercises + Assignment 1  
  Wed Programming 2 Olcese Exercises + Assignment 1  
  Thu Linear algebra 1 Heck Exercises  
  Fri Linear algebra 2 Heck Exercises  
2 Mon Linear algebra 3 Heck Exercises  
  Tue Linear algebra 4 Heck Exercises  
  Wed Linear algebra 5 Heck Exercises  
  Thu Advanced Programming Olcese Assignment 1  
  Fri Programming applications Olcese Assignment 1  
3 Mon Limits / power series (4 hrs) Heck Exercises + Assignment 2  
  Tue Functions in R2 Heck Exercises + Assignment 2 Deadline for submitting assignment 1 (11.00 a.m.)
  Wed Integration + Systems of diff. equations (4 hrs) Heck Exercises + Assignment 2  
  Thu     Assignment 2  
  Fri     Assignment 2  
4 Mon Vragenuur   Assignment 2  
  Tue       Deadline for submitting assignment 2 (11.00 a.m.)
  Wed        
  Thu  Deeltoets      
  Fri        
5 Mon Fourier analysis 1 Olcese Exercises  
  Tue Fourier analysis 2 Olcese Exercises  
  Wed Spectral filtering 1 Olcese Exercises  
  Thu Spectral filtering 2 Olcese Exercises  
  Fri Coherence + image processing Olcese Exercises + Assignment 3  
6 Mon Research Seminar Bosman Assignment 3  
  Tue     Assignment 3  
  Wed     Math exercises  
  Thu Systems dynamics / Stability analysis 1 (4 hrs) Heck Exercises  
  Fri Systems dynamics / Stability analysis 2 (4 hrs) Heck Exercises  
7 Mon Spiking data: Single-units Olcese Exercises  
  Tue Spiking data: Spike sorting (PCA) Olcese Exercises  
  Wed Spiking data: Population/information theory Olcese Exercises + Assignment 3  
  Thu Spiking data: Blind source separation/Dimensionality reduction Olcese Exercises + Assignment 3  
  Fri Research Seminar Zeldenrust Assignment 3  
8 Mon     Assignment 3  
  Tue       Deadline for submitting assignment 3 (11.00 a.m.)
  Wed Vragenuur      
  Thu        
  Fri Tentamen      

 

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

Contactinformatie

Coördinator

  • Umberto Olcese

Docenten

  • A.J.P. Heck
  • Jonas van Nijnatten
  • Conrado Bosman
  • Fleur van Zeldenrust
  • Glenn van der Lande