Analysis of Neural Signals

12 EC

Semester 1, periode 1

5102ANS12Y

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

Studiewijzer 2020/2021

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

  • understand and apply advanced concepts in linear algebra;
  • understand 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;
  • independently develop MATLAB and Python programs to perform simple signal processing routines.
  • combine algorithms to solve a problem and generate a program
  • apply the steps in signal processing pipelines
  • explain and apply 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;

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 + independent activities) and self-study. 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.

Most activities will be performed remotely (tools such as Zoom, Discord and Sowiso will be used). Details will be discussed during the introductory lecture and will be posted on Canvas. One laptopcollege per week will take place at Science Park.

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. 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.

 

Bovenstaande aanwezigheidsplicht geldt ook voor alle 'live' aangeboden online (computer)practica en werkcolleges.

Mocht je wegens persoonlijke omstandigheden (denk hierbij aan ziekte of bijzondere familieomstandigheden) niet kunnen deelnemen aan een verplichte onderwijsbijeenkomst, neem dan direct per e-mail contact op met de vakcoördinator of docent via de gecommuniceerde e-mailadressen. Er wordt dan met je besproken of er mogelijkheden zijn om het onderwijs op een andere wijze te volgen, en zo ja welke.

Ben je langdurig niet in staat om onderwijs te volgen (langer dan 1 week), neem dan ook contact op met de studieadviseur.

NB Covid-19: Houd je te allen tijde aan de RIVM richtlijnen, ook als dit betekent dat je daardoor één of meerdere verplichte onderwijsbijeenkomsten moet missen. Ook hiervoor geldt, neem direct contact op zodat er samen gekeken kan worden naar een oplossing.

Toetsing

Onderdeel en weging Details Opmerkingen

Eindcijfer

1 (50%)

Average Assignment grade

Must be >=5.0

1 (50%)

Assignment 1

1 (50%)

Assignment 2

1 (50%)

Average exam grade

Must be >=5.0

1 (50%)

Deeltoets

1 (50%)

Tentamen

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 or Python

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

 

Week Day Hoorcollege topic / other activity Hoorcollege lecturer Laptopcollege activities Notes
1 Mon Introduction Olcese Software installation  
  Tue Linear algebra - vector spaces Heck Exercises (Sowiso)  
  Wed Linear algebra - vector spaces Heck Exercises (Sowiso)  
  Thu Linear algebra - Systems of linear equations Heck Exercises (Sowiso)  
  Fri Linear algebra - Matrices Jager Exercises (Sowiso)  
2 Mon Linear algebra - Matrices Jager Exercises (Sowiso)  
  Tue Matlab programming Olcese Exercises (Matlab) + Assignment 1  
  Wed Matlab programming Olcese Exercises (Matlab) + Assignment 1  
  Thu Linear algebra - Eigenvalues Heck Exercises (Sowiso) + Assignment 1  
  Fri Linear algebra - SVD & applications Jager Exercises (Sowiso) + Assignment 1  
3 Mon Calculus - Multivariate 1 Jager Exercises (Sowiso) + Assignment 1  
  Tue Calculus - Advanced one-dimensional Heck Exercises (Sowiso) + Assignment 1  
  Wed Python programming Olcese Exercises (Python) + Assignment 1  
  Thu Python programming Olcese Exercises (Python) + Assignment 1  
  Fri Calculus - Multivariate 2 Jager Exercises (Sowiso) + Assignment 1  
4 Mon     Assignment 1  
  Tue       Deadline for submitting assignment 1 (11.00 a.m.)
  Wed Vragenuur      
  Thu        
  Fri Deeltoets      
5 Mon Recap on differential equations Jager Exercises (Sowiso)  
  Tue Bifurcations Heck Exercises (Sowiso)  
  Wed Linear systems of differential equations Heck Exercises (Sowiso)  
  Thu Nonlinear differential equations Heck Exercises (Sowiso)  
  Fri Applications of differential equations Heck Exercises (Sowiso)  
6 Mon Fourier analysis 1 Olcese Exercises (on paper)  
  Tue Fourier analysis 2 Olcese Exercises (on paper)  
  Wed Spectral filtering 1 Olcese Exercises (Matlab) + Assignment 2  
  Thu Spectral filtering 2 Olcese Exercises (Matlab) + Assignment 2  
  Fri Phase coherence + image processing Olcese Exercises (Matlab) + Assignment 2  
7 Mon Spiking data: Single-units Olcese Exercises (Matlab) + Assignment 2  
  Tue Spiking data: Spike sorting (PCA) Olcese Exercises (Matlab) + Assignment 2  
  Wed Spiking data: Population/information theory Olcese Exercises (Matlab) + Assignment 2  
  Thu Introduction to neural-based classifiers Olcese Exercises (Python) + Assignment 2  
  Fri Research seminar Bosman Assignment 2  
8 Mon     Assignment 2  
  Tue       Deadline for submitting assignment 2 (11.00 a.m.)
  Wed Vragenuur      
  Thu        
  Fri Tentamen      

 

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

The course material related to programming in Python has been expanded. The math part has been reorganized with the inclusion of a new lecturer.

Contactinformatie

Coördinator

  • Umberto Olcese

Docenten

Lectures

  • André Heck
  • Gideon Jager
  • Conrado Bosman

Teaching assistants

  • Joao Patriota
  • Anna van Harmelen