Minor The Computing Brain
- Broadening minor
This minor will teach you how to simulate neurons and to use them to reproduce cognitive functions of the brain such as object recognition and decision making. The student will learn and actively explore the multiple developments of computational neuroscience and artificial intelligence and their relationships with the underlying 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 behavior, to essential neurobiology, to simulation of neurons and networks, onto the fundamentals of neural networks and their application to the problems of cognition.
The guiding questions covered in this minor are:
- What is computation? What is the difference between biological and in silico computation?
- What is the organization of intelligence and cognition in organisms?
- How are the computational principles implemented in biological substrates?
- How do neural networks help explain brain function?
- What kinds of brain functions can be reproduced by current neural networks?
- 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, 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.
After following this minor, the student should be able to:
- Explain the basic organization of intelligence and cognition as a function of action-perception loops
- Understand the basic differences between biological and artificial computation
- Interpret brain signals from different methods of brain measurement (e.g., EEG, fMRI)
- Explain the dynamical process of action potential generation
- Understand how biological neurons are translated into computational models
- Simulate a biological neuron
- Explain different neuronal activity patterns on the basis of biophysical parameters
- 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 spatio-temporal 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)
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 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/P62GDTS). Nota bene for Delft students: if you need to fill your 30 ECTS, please check the Delft minorsite for complementary courses.
- Basic knowledge of biology (e.g., what is a cell? What is a protein?)
- Basic knowledge of physics and electricity (e.g., What is an ion? What is the electrical current? What is a capacitor?)
- Basic understanding of a programming language (e.g., what is the difference between a function and a script? What is a type?)
- Basic understanding of linear algebra (e.g., what is a vector? What is a matrix?)
- Basic understanding of differential equations (e.g., if x is distance, what is dx/dt?)
- Part 1: Neurobiology and Cognition (lectures and student presentations)
- First week: Basic Neurobiology + Brain Measurement (lectures + lab visit)
- Second week: Organization of Behavior and Cognition (Sensorimotor behavior)
- Third week: Principles of Neuroscience I (lectures and student presentations)
- Fourth week: Principles of Neuroscience II (lectures and student presentations)
- Part 2: Simulation of Biological Neurons (lectures and student projects)
- Fifth week: Simulating Neurons I (Biophysical models of the neuron)
- Sixth week: Simulating Neurons II (Synapses + Network simulations)
- Part 3: Artificial Neural Networks (video lectures and student projects)
- Seventh week: artificial neural networks I (perceptron and backpropagation)
- Eight week: artificial neural networks II (multilayer neural networks)
- Ninth week: artificial neural networks III (recurrent neural networks)
- Tenth week: project presentations and final exam
Weekly Introductory lectures, flip-the-classroom presentations, self-study with recommended material (books, articles and video lectures), lab visits, in-class guided discussions.
Book chapters from Principles of Neuroscience, Handbook of Neural Networks (ed. Arbib)
Video lectures (Neurodynamics from Wulfram Gerstner and Deep learning by Andrew Ng)
Wijze van tentaminering
- Peer + Lecturer evaluation of student presentations (1/4 of grade
- Peer + Lecturer evaluation of student projects (1/4 of grade)
- Digital examination (1/2 of overall grade)
Important to note:
- Presence is obligatory during teaching
- Note that each of the components needs to have minimum grade of at least 5.5.
- Lecturer and Peer evaluation of student presentations (1/4 final grade)
- Lecturer and Peer evaluation of student projects (1/4 final grade)
- Final online examination (1/2 final grade)
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.
06 14 49 0060
- Broadening minor
- Erasmus MC
- Studiepunten (ECTS)
- Erasmus MC, Rotterdam