Course manual 2022/2023

Course content

This course will provide students with the basic knowledge and tools to understand and produce computational neuroscience models from the complementary perspectives of neural dynamics and brain-inspired deep learning.

Study materials

Literature

Dayan and Abbott, “Theoretical Neuroscience”, MIT Press, 2001 (free version available online)
Gerstner, Kistler, Naud and Paninski, “Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition”, Cambridge Press, 2014 (freely available online)
Izhikevich, “Dynamical systems in neuroscience: the geometry of excitability and bursting” MIT Press, 2007

Objectives

Describe and compare different models, methods and approaches in computational neuroscience.
Simulate computational neuroscience models, analyze their results and draw conclusions from them.
Present and critically discuss results of computational neuroscience research.

Teaching methods

Lecture
Seminar
Presentation/symposium
Supervision/feedback meeting
Computer lab session/practical training

Theoretical lectures and seminars will provide the basic and advanced knowledge on topics of computational neuroscience. Computer lab session and feedback meetings will allow the student to put this knowledge in practice, by developing a hands-on computational project guided by an experienced tutor. Assessment will be done based on presentations by students during interim presentations, the final presentation, and a written report.

Learning activities

Activity	Hours
Hoorcollege	40
Laptopcollege	76
Presentatie	12
Self study	40
Total	168	(6 EC x 28 uur)

Attendance

Requirements of the programme concerning attendance (OER-B):

Attendance during practical components exercises is mandatory.

Additional requirements for this course:

Attendance during the lectures, seminars and interim/final presentations is mandatory. In case a student needs to miss one of them, it should be communicated in advance to the course coordinator (Mejias).

Assessment

Item and weight	Details
Final grade
10% Interim presentation 1	Mandatory
10% Interim presentation 2	Mandatory
30% Final project presentation	Mandatory
50% Written report	Mandatory

The final cut-off score for passing this course is 5.5. Missing deadlines or late submissions will be penalized.

Inspection of assessed work

Partial grades will be notified to the student via Canvas.

Assignments

Assignments of this course include two interim presentations, one final presentation, and one written report. For presentations, slides will have to be uploaded as Canvas assignments before the start of the presentation class. Presentations will be graded in a group, and the written report will be graded individually.

Fraud and plagiarism

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

Course structure

Weeknummer	Onderwerpen	Studiestof
1	Basic concepts of neural dynamics and deep learning, project work	Slides provided by lecturers
2	Advanced seminars, project work, and interim presentation 1	Slides provided by lecturers
3	Advanced seminars, project work, and interim presentation 2	Slides provided by lecturers
4	Project work, final presentation, written report
5
6
7
8

Owner	Master Biomedical Sciences
Coordinator	dr. Jorge Mejias
Part of	Master Artificial Intelligence, Master Biomedical Sciences, track Oncology, Master Biomedical Sciences, track Cognitive Neurobiology and Clinical Neurophysiology, Master Biomedical Sciences, track Psychopharmacology and Pathophysiology, Master Biomedical Sciences, track Infection and Immunity, Master Biomedical Sciences, track Cell Biology and Advanced Microscopy, Master Biomedical Sciences, track Biochemistry and Metabolic Diseases, Master Biomedical Sciences, track Experimental Internal Medicine, Master Biomedical Sciences, track Molecular Neurosciences, Master Biomedical Sciences, track Physiology of Synapses and Networks, Master Biomedical Sciences, track Developmental and Therapeutic Biology, Master Biomedical Sciences, track Medical Biochemistry and Biotechnology, Master Biomedical Sciences, track Cardiovascular Sciences, year 1

Neural Dynamics and Deep Learning