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 2026
Implementing and extending a biologically inspired computational model of spatial navigation in rodents
In this project, you will develop a computational model that simulates a virtual rat navigating through different environments, integrating visual and self-motion inputs to generate behavior via two distinct pathways: a taxon pathway, which associates visual input directly with motor actions through dorsal striatum–like mechanisms, and a locale pathway, which builds a spatial representation using simulated grid and place cells feeding into the ventral striatum. Your task will be to implement this model in code (model from [1], described in the main text and in the appendix), including components such as orientation-sensitive visual filters, view cells, grid cells, and place cells, and validate its behavior in reorientation and watermaze paradigms as described in the paper [1]. Once the base model is validated, there will be opportunities to extend it in various directions.
Good programming skills in Python, experience in Reinforcement Learning, and knowledge in neuronal dynamics and computer graphics are required. Interested students should send their application, including CV and grades in relevant classes, to [email protected].
[1] Denis Sheynikhovich, Ricardo Chavarriaga, Thomas Strosslin, Angelo Arleo and Wulfram Gerstner (2009), Is there a geometric module for spatial orientation? Insights from a rodent navigation model, Psychological Review, 116:540-566.
Link: https://lcnwww.epfl.ch/gerstner/PUBLICATIONS/Sheynikhovich09.pdf
List of Projects – Autumn 2025
Modeling perceptual rules of social interactions in single animal virtual reality
Social affiliation and coordination are essential in humans and animals. However, how social interactions emerge from neural computation is largely unknown. The Larsch lab at UNIL recently discovered, that zebrafish, an emerging model for studying swarm behavior, are highly attracted to virtual objects that mimic the kinetics typical of another zebrafish that swim in a characteristic manner of alternating bursts and glides. This project will investigate if this visual preference arises from hard-wired tuning to motion statistics representative of an average age-matched fish (1), or, alternatively, if it arises from an idiosyncratic process that couples the fishes’ own swim cycle to the stimulus (2).
To discriminate between these two hypotheses, we will use computational modelling, including modern machine learning tools. The Larsch lab has video-recorded and tracked hundreds of hours of fish responding to dots projected on a screen that flicker on and off (and/or move on the screen) that can be used to fit and compare different models of fish behavior. If time permits, we will use and extend these models to analyze and predict neural signals in the fish brain.
Good programming skills in Python or Julia, experience with Deep Learning, and basic Statistics knowledge are required. Interested students should send their application, including CV and grades in relevant classes, to [email protected] and [email protected].
References:
– https://www.nature.com/articles/s41586-022-04925-5
– https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007354
List of Projects – Spring 2025
Identifying monosynaptic connection using deep learning method (also possible as a Semester project) – TAKEN
Recently, we developed an approach to measure synaptic connectivity in vivo, training a deep convolutional network to reliably identify monosynaptic connections from the spike-time cross-correlograms of millions of single-unit pairs.
The benchmark results on both the experimental recordings and synthetic datasets indicate that the method is very promising.
In this project, you will learn
- methods about synaptic connectivity inference,
- and how to simulate a large network of spiking neurons for benchmarking,
while
- trying to improve the performance of the current method further. (I already have a few ideas for you to start with.)
The project is perfect for a student with a decent Python programming background and who wants to study applied machine learning problems in neuroscience.
The minimum requirements for the student are to write clear codes and be interested in this project.
Interested candidates, please send their application to [email protected]. If you have any questions, also feel free to contact me.
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.