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P&S:Interfacing Biological Neuronal Networks with Machine Learning
Last Updated: 2026-06-03 00:14:20
Abstract
The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.
Objective
The way memory and learning is achieved in the brain is an unsolved problem. Due to its relative simplicity, in-vitro neuroscience can help us discover the fundamentals of information processing in the brain. For this we can build a network of biological neurons on top of an array of microelectrodes. Such an approach allows us to record the electrical activity of the neurons when they get stimulated. Following this approach, we can investigate biological neural networks, that have about 5-50 neurons and a controlled network architecture. Still, their behavior remains highly unpredictable. Therefore, it is not yet clear how such networks need to be stimulated electrically in order to control their behavior. However, we can use machine learning to find a mapping between a stimulus and a desired response. More specifically, we can use reinforcement learning to find stimulation patterns that achieve a specified goal. This P&S consists of two parts. In the first part we will introduce you to the way neurons can be cultured in vitro. You will learn how to seed and grow the culture of neurons on a multi-electrode array (MEA). Next you will stimulate given networks and record data. The second part will be about machine learning. We will discuss the basics of both artificial neural networks (ANN) and reinforcement learning (RL). As homework exercises you will design and train ANNs and develop reinforcement learning agents to control your biological networks. Your controlled biological networks will participate in a competition in performing a final challenge. This P&S will be given in English. In total, the P&S takes 10 afternoons and about 60 hours of homework (ANN implementation).
Content
Important information to students: - This P&S will be given in English. - In total, the P&S takes 8 afternoons and about 40 hours of homework (ANN implementation).
General Information
- Language
- English
- Levels
- BSC
- Frequency
- Yearly recurring
Examination
- Type
- ungraded semester performance
Registration & Places
- Signup Start
- 13.02.2026
- Signup End
- 27.02.2026
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| practical/laboratory course |
P&S: Interfacing Biological Neuronal Networks with Machine Learning
Für den Zugang zum Angebot und zur Einschreibung loggen Sie sich hier ein (mit Ihrem n.ETHZ account):
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To access the offer and to enroll for courses log in (with your n.ethz account):
Please note that the P&S-site is accessible no earlier than two weeks before the start of the semester until four weeks after the start of the semester. Enrollment is only possible from Friday before the start of the semester until noon of the first Friday in the semester.
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4 h weekly |
Offered In
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Laboratory Courses, Projects, Seminars (A minimum of 15 cp must be achieved in the category "Laboratory Courses, Projects, Seminars")
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Projects & Seminars (only for BSc EEIT) (Enrolment is only possible for students in the BSc Electrical Engineering and Information Technology, from Friday before the start of the semester. Places are allocated using the P&S application tool ( ). For more offers, see "Projects & Seminars (open to all)".)
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