12 EC
Semester 1, period 1
5103FDS12Y
The main subjects covered in the course will be:
Material provided by lecturer
A short compendium of most of the topics covered during the course can be found in the following book: Nylon & Wallish, Neural Data Science (A Primer with MATLAB and Python), Elsevier, 2017 (PDF will be made available).
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
Material will be provided by lecturers
Python
Lectures will be used to introduce and discuss concepts and topics related to math, programming and signal analysis.
During computer practicals, students will work on exercises meant to learn the applied skills associated with the taught concepts and methods, and will work on the group assignments.
During encephalography (EEG) practicums, students will collect their own EEG data, to be analysed in assignment 1.
Self-study will allow students to memorize and better understand the explained topics.
Independent (and group) work will be needed to complete the group assignments.
|
Activity |
Hours |
|
|
Lectures |
66 |
|
|
Practicals |
66 |
|
|
Exam |
4 |
|
|
Self study |
200 |
|
|
Total |
336 |
(12 EC x 28 uur) |
Weeks 1-3, 5-7:
Lectures (2 hours per day), laptop seminars (own laptop is required, 2 hours per day with assistance + independent activities) and self-study. During laptop seminars, students will work individually on exercises that help get the skills that are needed for the (graded) group assignments. Assignments will be prepared during laptop seminars and during self study.
Weeks 4, 8:
Self study (preparation for exams, completion of assignments).
Additional requirements for this course:
Participation to lectures and laptop seminars 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.
| Item and weight | Details | Remarks |
|
Final grade | Average must be >5.5 | |
|
0.7 (70%) Average exam grade | Must be ≥ 5 | |
|
0.35 (50%) Partial exam 1 | Mandatory | |
|
0.35 (50%) Partial exam 2 | Mandatory | |
|
0.3 (30%) Average assignment grade | Must be ≥ 5 | |
|
0.15 (50%) Assignment 1 | Mandatory | |
|
0.15 (50%) Assignment 2 | Mandatory |
Grading matrices/rubrics and criteria for each of the graded components will be uploaded on Canvas at the beginning of the course.
Assignments will be done in groups of 3 students, and will lead to a single group grade. Assignments will be primarily based on the development of Python 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 grade will be made up by the following components:
Partial exam 1: 35%
Partial exam 2: 35%
Assignment 1: 15%
Assignment 2: 15%
In order to pass the course, the average grade for the exams (partial exam 1 + partial exam 2) must be >=5.0 and the average grade for the assignments (assignment 1 + assignment 2) must be >= 5.0. The final weighted grade (partial exam 1 + partial exam 2 + assignment 1 + assignment 2) must be >=5.5.
In case the average grade for the exams is <5.0, students will have the opportunity to attend the resit. In case the average assignment grade is <5.0, students will have to retake the course the next academic year.
The resit will combine topics covered in the partial exam 1 and partial exam 2 and will account for 70% of the course grade. The minimum passing score for the resit is 5.0. The other 30% of the grade will come from the assignments.
Contact the course coordinator to make an appointment for inspection.
Assignment 1
Students will be asked to collect and analyse (in Python) and interpret EEG data.
Assignment 2
Students will be asked to develop a program (in Python) to analyse neurophysiological data (local field potentials and spiking activity)
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
| Week | Day | Hoorcollege topic / other activity | Hoorcollege lecturer | Laptopcollege activities |
| 1 | Mon | Introduction | de Gee | Software installation |
| Tue | Python for neuroscientists Pandas / MNE | de Gee | Exercises (Python) | |
| Wed | Collect EEG data | de Gee | Exercises (Python) | |
| Thu | Collect EEG data | de Gee | Exercises (Python) + Assignment 1 | |
| Fri | Collect EEG data | de Gee | Exercises (Python) + Assignment 1 | |
| 2 | Mon | Fourier analysis 1 | de Gee | Exercises (on paper) + Assignment 1 |
| Tue | Linear algebra - vector spaces | Lucic | Exercises (Sowiso) + Assignment 1 | |
| Wed | Linear algebra - vector spaces | Lucic | Exercises (Sowiso) + Assignment 1 | |
| Thu | Linear algebra - Systems of linear equations | Lucic | Exercises (Sowiso) + Assignment 1 | |
| Fri | Linear algebra - Matrices | Lucic | Exercises (Sowiso) + Assignment 1 | |
| 3 | Mon | Linear algebra - Matrices | Lucic | Exercises (Sowiso) + Assignment 1 |
| Tue | Fourier analysis 2 | de Gee | Exercises (on paper) + Assignment 1 | |
| Wed | Linear algebra - Eigenvalues | Lucic | Exercises (Sowiso) + Assignment 1 | |
| Thu | Linear algebra - SVD & applications | Lucic | Exercises (Sowiso) + Assignment 1 | |
| Fri | Linear algebra - Extra lecture | Lucic | Exercises (Sowiso) + Assignment 1 | |
| 4 | Mon | |||
| Tue | ||||
| Wed | Vragenuur | |||
| Thu | ||||
| Fri | Deeltoets | |||
| 5 | Mon | Spiking data: Single-units | de Gee | Exercises (Python) |
| Tue | Spectral filtering 1 | de Gee | Exercises (Python) + Assignment 2 | |
| Wed | Free | Lucic | Assignment 2 | |
| Thu | Calculus - Multivariate 1 | Lucic | Exercises (Sowiso) + Assignment 2 | |
| Fri | Calculus - Advanced one-dimensional | Lucic | Exercises (Sowiso) + Assignment 2 | |
| 6 | Mon | Calculus - Multivariate 2 | Lucic | Exercises (Sowiso) + Assignment 2 |
| Tue | Calculus - Extra lecture | Lucic | Exercises (Sowiso) + Assignment 2 | |
| Wed | Spiking data: Spike sorting (PCA) | de Gee | Exercises (Python) | |
| Thu | Spectral filtering 2 | de Gee | Exercises (Python) + Assignment 2 | |
| Fri | Phase coherence + image processing | de Gee | Exercises (Python) + Assignment 2 | |
| 7 | Mon | Free | de Gee | Assignment 2 |
| Tue | Introduction to decoding | de Gee | Exercises (Python) + Assignment 2 | |
| Wed | Introduction to neural-based classifiers | de Gee | Exercises (Python) + Assignment 2 | |
| Thu | Spiking data: Population/information theory | Olcese | Exercises (Python) + Assignment 2 | |
| Fri | TBA | de Gee | Assignment 2 | |
| 8 | Mon | Research seminar | Bosman | Assignment 2 |
| Tue | ||||
| Wed | Vragenuur | |||
| Thu | ||||
| Fri | Tentamen |
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
Notes:
1) The course is part of the Computationele Psychobiologie leerlijn
2) Information might still be found under the previous name of the course: "Analysis of Neural Signals"
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 (e.g. Inleiding Programmeren or similar).
Capacity: max. 50 students