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 – Autumn 2024
Modeling the impact of stimulus similarities on novelty perception using deep learning for latent representations (taken)
Novelty is an intrinsic motivational signal that guides the behavior of humans, animals and artificial agents in the face of unfamiliar stimuli and environments. But how can an (biological or artificial) agent determine whether a stimulus is novel or not? In the lab, we recently showed how algorithmic models of novelty detection in the brain [1,2] can be extended to continuous environments and stimulus spaces with similarity structures [3]. However, our current model relies on existing stimulus representations that are either constructed based on experimental knowledge or derived from pre-trained, brain-like deep networks. In machine learning, on the other hand, novelty is computed using neural networks that are trained end-to-end to estimate the stimulus novelty [4,5], which makes it hard to understand how the structure of the stimulus space influences novelty computation.
In this project, we will take a hybrid approach to model how novelty can be computed in naturalistic stimulus spaces, and combine deep learning with algorithmic models of novelty computation. We will investigate how algorithmic novelty signals can be used to instruct representational learning and how these representational changes in turn affect the computation of novelty. Finally, we will compare the novelty signals predicted by our model to state-of-the-art algorithmic and machine-learning models of novelty computation.
Good programming skills in python and prior experience with deep learning and pytorch are required. Interested students should send their application, including CV and grades in relevant classes, to [email protected].
References
[1] Xu et al., Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making. PLOS Comp. Biol. (2021)
[2] Modirshanechi et al., Surprise and novelty in the brain. Curr. Op. Neurobiol. (2023)
[3] Becker et al., Representational similarity modulates neural and behavioral signatures of novelty. biorxiv (2024)
[4] Bellemare et al., Unifying count-based exploration and intrinsic motivation. NeurIPS (2016)
[5] Ostrovski et al., Count-based exploration with neural density models. PMLR (2017)
List of Projects – Spring 2024
A video game experiment on mental time-travel and one-shot learning (taken)
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.