Minor The Computing Brain

Verbredende minor
10 weken


Our modern understanding of the brain combines biology with computational models to discover how neurons and networks underlie behavior. This minor will depart from basics of neuroscience with lectures about neurons, brain and cognition and build up to teach you how to simulate neurons and neural networks. We will use networks to reproduce neuronal dynamics and high-level cognitive functions of the brain such as pattern recognition, motor behavior, memory and navigation. The student will learn and actively explore the multiple developments of computational neuroscience and artificial intelligence and their relationships with the biological brain. The minor emphasizes a mechanistic understanding of brain function and computation by presenting modern neural networks as explanatory devices. We work systematically from essential neurobiology, to behavior, to simulation of neurons onto the fundamentals of neural networks and their application to the problems of intelligence and cognition.

Some of the guiding questions covered in this minor are:
• How does the neuron work?
• What is computation? What are the differences between biological and in silico computation?
• How does the organization of the nervous system give rise to intelligent behavior?
• How are computational principles implemented in biological substrates?
• How do artificial neural networks help explain brain function?
• How can we build interfaces between brains and computers?

This minor is aimed at students from biomedical and technical backgrounds, with an interest in practical implementations ("coding"), to delve into the current state of brain theory. The minor explores the interfaces between neuroscientific and computational disciplines, with a distinctive emphasis on hardware and software implementation of neural network models of brain computation.

Learning objectives

After following this minor, the student should be able to:
• Describe the fundamental properties of a biological neuron
• Explain the dynamical process of action potential generation
• Understand how biological neurons are translated into computational models
• Explain different neuronal activity patterns on the basis of biophysical parameters
• Interpret brain signals from different methods of brain measurement (e.g., EEG, fMRI)
• Implement neural networks that perform pattern recognition networks
• Understand and implement backpropagation to training feed forward neural networks
• Understand the principles behind modern "deep learning" architectures
• Understand how recurrent neural networks are used to generate spatiotemporal patterns
• Be able to choose between supervised, unsupervised and reward-based learning methods for particular problems
• Explain cognitive functions through via neural network models and analogies (e.g., object recognition / decision making / motor behavior)

Special aspects

The language of the minor is ENGLISH. Nota bene for students of medical and biological backgrounds: although the minor is open for students of various backgrounds, it is aimed at, and tailored for, students with quantitative background and some coding skills. It is highly recommended that students taking this minor should have knowledge of basic physics, linear algebra, biology and programming.

To help with deciding whether to take this minor or not, please take the self-evaluation quiz in SurveyMonkey (https://www.surveymonkey.com/r/WFFSFFDOpens external ).

Nota bene for Delft students: if you need to fill your 30 ECTS, please check the Delft minorsite for complementary courses.

Overview content per week

Week1: The Neuron and Neuronal Models (lectures + lab visit + coding project). After a discussion of the goals of computational neuroscience, the students are introduced to the biology of the neuron and the Hodgkin Huxley model of the action potential. Via a python project, students learn to simulate the equations representing the membrane potential of the neuron.
Week2: Animal Behavior, The Organization of Brains, and Brain Measurement. Students develop a sense for what it means to explain neuron-based behavior via experiments and measurement. Students are introduced to brain anatomy and electrophysiological experiments.
Week3: Encoding and Decoding, Features and Receptive Fields. Students learn the basics of encoding and decoding via neural networks, from tuning curves to signal reconstruction (e.g. spike triggered averages). Students manually design a neural network that has a receptive field to recognize orientation.
Week4: Synapses and Synaptic models What is in a synapse, what can a synapse do? How can a synapse change (morphologically, ion channel traficking)How does synaptic change relate to learning?
Week5: Memory, self-organization and Hebbian Learning. Students master a taxonomy with the different kinds of learning. Students learn to dissociate learning from plasticity. Students consider the questions of memory capacity, and the 'generative' nature of memory recall. The role of the Hippocampus in learning is introduced. Students study memory models via attractor networks (Hopfield networks).
Week6: Hierarchical processing and Feed Forward Neural Networks. Students learn about hierarchies of visual processing, the 'what and where' pathways. Other types of such hierarchical processing schemes are discussed. Students are able to reason about feature decomposition in the brain via processing hierarchies via 'tuning curves', 'receptive fields' and 'feature associations'.
Week7: Recurrent Neural Networks and Memory. Students learn that recurrent neural networks can serve as models for memory. With an in-class project students learn to encode attractors in a Hopfield RNNs with Hebbian Learning. Students observe the decay of the energy function associated with settling on an attractor. Students get a sense for bistable dynamics in perception through a Necker's cube pattern recognition implementation.
Week8, Week 9: Students decide on their group projects (1 or 2 students per project). Every session starts with a journal club and ends with guided project work.
Week10: Exam

Teaching methods

The course includes 1. lectures, 2. programming projects, 3. Group discussion. Classes often start with comprehension quizzes and discussion of previous lectures and their overarching context. Students are expected to delve in guided self-study with recommended material (books, articles and video lectures). In the beginning of the course we have lab visits to the neuroscience department. Computational modeling is mostly conducted in the python language in 'google collaboratory' (students do not need to install python). Students prepare for the final exam via spaced-repetition flash cards (www.brainscape.org). Students exercise their ability to read and communicate scientific articles via a journal Club that revises and extends on the concepts previously acquired.

Teaching materials

- Online reading material.
- Programming projects ('google collaboratory')
- Video-lectures.
- Tutorials.
- Flashcards (Brainscape).
- Book chapters from "Principles of Neuroscience"
- Selected scientific articles.

Method of examination

Method of examination

  • Digital Examination – Exam with multiple choice and open questions at the end of week 10.
  • Final Project Work and Presentation - In groups of two or individually, students deepen their grasp of the topics in a chosen project.  It is assumed that students have heterogeneous backgrounds. In the project presentations we expect the students to be able to demonstrate their learning arc. They must be able to show how the projects involve the new knowledge gained in the course.
  • Each student must present one paper in the Journal club (pass/fail)

    Important to note:
  • Presence is obligatory during all teaching. Maximum of 2 absences (at student’s discretion).
  • To pass the minor students must achieve a minimum grade of at least 5.5 in each of the components.

Composition final grade

  • Digital Examination – 50%
  • Project work (Evaluated via Presentation) – 50%

Students will have access to collected peer and lecturer feedback after presentations.
Projects will be evaluated according to the quality of the implementation and presentation.
Upon request, there will be an opportunity to receive feedback on the final digital exam.


M. Negrello
06 14 49 0060

Verbredende minor
10 weken
Erasmus MC
Studiepunten (EC)
Erasmus MC, Rotterdam