Neural Models of Cognitive Processes


Course Objective

Computational modeling is an important tool for cognitive neuroscience,
but the majority of modeling work requires quite some background
knowledge on the core principles being applied.
The course is intended to offer insight(s) into what different types of
models exist in in cognitive neuroscience, how they can be (and are)
used to enrich the field, and it explores what questions arise when
evaluating modeling work in this field.

Of note, even though the course offers practical sessions where you work
with some models implemented with Python code, this course is explicitly
_not_ intended as a programming class intended to test your programming

Course Content

Computational models are an important tool in cognitive neuroscience. A
large branch of research focuses on an experimental approach, testing
predictions by means of carefully designed experiments. Models, on the
other hand, can integrate experimental results into complete and
detailed theories that produce testable predictions. As such, they form
a critical step in the empirical cycle by generating predictions for
future experiments.
When used appropriately, a model allows for the integration of findings
from a wide range of experiments. Rather than merely verbal theories,
computational models are rich in detail and allow for a mechanistic view
on how the brain produces its behavior.

An old adage from statistics is that ``all models are wrong, but some
models are useful''. They are wrong because a model by definition is a
simplification of reality, but they are useful when they generate
testable predictions. However, it can be difficult to assess whether a
model is too much of a simplification, and whether its predictions
actually are useful. What makes a model good or bad? To what extent do
models need to fit the data? And if multiple models fit the data, how do
we choose which is the `better one'?
In addition, modeling papers can at times seem rather enigmatic, and for
the untrained reader it is all too easy to get lost in the mathematical
equations that make up computational models.

This course takes a learn-by-example approach to give an overview of
different modeling approaches that are common in neuroscience. We will
start at a high level of abstraction, with models that are used to
mathematically describe experimental data, with relatively little regard
for their implementation in the brain. throughout the course, will work
our way `down' towards models of individual spiking neurons. By means of
practical sessions, you will get hands-on experience with some of these
models and see how they are implemented. By means of `debates', you will
learn how to assess different models in terms of their strengths and

Teaching Methods

Lectures and discussion, computer tutorial and practicals.

Method of Assessment

Grades are based on a weighted average of performance on a final exam
(65%), the practical sessions (25%), and class participation in the
debate sessions (10%)

Entry Requirements

There is no explicit required knowledge. However, as the practicals have
you work with Python code, it might be useful to familiarize oneself
with the language. The 'programming for psychologists' course should
suffice, and offers a wonderful
free online tutorial


A large part of the courses uses chapters from the book
Fundamentals of Computational Neuroscience, Thomas P. Trappenberg

Additional literature (articles, tutorials) will be provided through

Additional Information

This course is taught every two years, not in 2018-19. Students who
took the course in 2017-18 and did not successfully completed the course
can contact the course coordinator to maken an appointment to still do

General Information

Course Code P_MNEUMOD
Credits 6 EC
Period P2
Course Level 400
Language of Tuition English
Faculty Fac. of Behavioural and Movement Science
Course Coordinator dr. T.H.J. Knapen
Examiner dr. T.H.J. Knapen
Teaching Staff dr. T.H.J. Knapen

Practical Information

You need to register for this course yourself

Teaching Methods Lecture, Computer lab