Master Thesis Projects are started once the complete master program is finished and all the credits have been obtained.
Projects for SSC and SIN students should last 4 months at the EPFL or 6 months in the industry or in another University.
Master Thesis Projects must be done individually.
Master Thesis Projects are worth 30 credits.
Students must have the approval of the Professor in charge of the laboratory before registering for the given project.
List of Projects – Spring 2024
A video game experiment on mental time-travel and one-shot learning
Mental time-travel is the process of vividly remembering past personal experiences or imagining oneself in a future situation. Whereas humans can be asked to describe what they experience during mental time-travel, indirect approaches are needed to investigate whether mental time-travel exists in other species. We study a class of behavioural tasks that humans can presumably solve using mental time-travel, and that feature a behavioral readout other than verbal descriptions of subjective experiences. For example, a subject may perform an action to prepare for an event in the near future, by recalling a related but unique prior episode where they were unprepared.
In this project, we will design simple video game implementations of this behavioral paradigm, to study in rodent and human subjects. The project consists of four tasks. First, design and test implementations of the task using the Unity game engine. Second, run pilot human behavioural experiments with lab members and friends. Third, run the experiment online (e.g. on prolific.co) or with EPFL students. Four, analyse the behavioural data.
Good programming skills are a strict requirement; familiarity with Unity is an asset but not required. Interested students should send their application, including CV and grades, to both [email protected] and [email protected].
This project can also be done as a Semester project.
List of Projects – Autumn 2023
Bio-implementation of a Continual Learning Method: Adapting GaNON to Unsupervised Learning Using CLAPP and Three-Factor Learning Rules.
We recently developed in the lab a continual learning method that demonstrates the ability to learn new tasks while preserving existing knowledge called GaNON. We propose a project to further enhance the biological plausibility of this method. We aim to adapt GaNON to an unsupervised learning method called CLAPP. The project consists in adapting the GaNON method to local three-factor learning rules. This project is not a spiking model implementation.
- Investigate the principles and mechanisms of the GaNON continual learning method and CLAPP network.
- Derive local learning rules for the GaNON method in the CLAPP framework
- Evaluate the performance of the adapted GaNON method in various learning tasks and compare it with existing continual learning methods.
- Explore potential applications and future directions for the bio-implementation of GaNON.
- We expect the students to have a strong comprehension of deep neural networks.
- Knowledge in Bio-inspired neural network.
- Python programming ( PyTorch is not required but useful)
List of Projects – Spring 2023
Mesoscopic modeling of hidden spiking neurons
In order to model the recorded spike trains with spiking neural networks, one has to consider the latent effect from unobserved neurons.
In our previous work , we derived a neuronally-grounded latent variable model and showed that it can effectively model the large number of hidden neurons on synthetic datasets.
In this project, we hope to extend the previous mesoscopic model to a larger family of neuron models, i.e., adaptive leaky integrate-and-fire neuron .
The student should have a good background in programming (python). Basics in deep learning and/ or interests in computational neuroscience would be a plus.
Feel free to contact us for questions and infos.
 Wang, S., Schmutz, V., Bellec, G., & Gerstner, W. (2022). Mesoscopic modeling of hidden spiking neurons. arXiv preprint arXiv:2205.13493.
 Schwalger, T., Deger, M., & Gerstner, W. (2017). Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size. PLoS computational biology, 13(4), e1005507.