Students interested in carrying out a Master research project in the EPFL Laboratory of Sensory Processing should contact Carl Petersen and Sylvain Crochet to discuss possible opportunities. We offer a wide range of projects involving experimental work, data analysis and computational neuroscience. Some specific open projects are listed below:
Project 1 (please contact: [email protected]): Quantification of cell distribution in cleared whole brains – Tissue clearing and light-sheet microscopy enable the visualization of cellular populations across the entire mouse brain in 3D. Using iDISCO clearing [Renier et al. Cell 2014] and reporter mouse lines, we obtain brains from different Cre driver lines to investigate cell-type–specific distributions at whole-brain scale. Mapping the distribution of defined neuronal populations is key to understanding how different cell types are organized across cortical layers and brain regions, and how these organizational patterns may support circuit function. The goal of this project is to establish and apply an analysis pipeline that can (i) register light-sheet imaged brains to a standard atlas [Perens et al. Neuroinformatics 2021], (ii) detect and quantify labeled cells, and (iii) compare cell distributions between Cre driver lines. We are looking for a highly motivated student with strong interest in neuroanatomy and computational neuroscience. The project will provide hands-on experience with state-of-the-art brain clearing and imaging datasets, and the student will gain expertise in image analysis, atlas registration, and large-scale data visualization. This project requires good programming skills (Python), experience with image processing or data analysis, and an interest in neuroanatomical mapping.
Project 2 (please contact: [email protected]): Analysis of animal behaviour during learning – In laboratory settings, video filming is used to monitor movements during behavioural paradigms. Machine learning tools, such as DeepLabCut, an open-source software tool for pose estimation, have facilitated the analysis and quantification of behaviour. Recent work has emphasized the pervasiveness of body movement representations on neural activity as well as the richness of movement patterns that animals exhibit. The goal of this project is to explore and quantify orofacial movements of head-fixed mice that perform an associative learning task using pre-determined pose estimation traces. First, we will explore possible movement trends occurring on a trial-by-trial basis. Then, we will seek to model possible relationships between movements and mouse decisions as the mouse learn the association of a sensory stimulus with a reward on a trial-by-trial basis. The overarching goal of this project is to understand trial-timescale dynamics of expressed behaviour, beyond simple averages. Additionally, we have employed a minimal reinforcement learning model to capture individual learning dynamics. We would like to develop this modeling framework in the context of this task. We are looking for an outstanding and highly motivated student to help us in the data analysis. The project will enable the student to manipulate rich behavioural data and implement advanced analysis methods. This project requires advanced programming skills (Python), experience with machine learning, statistics, data visualization and an interest for neuroscience and behavior.
Project 3 (please contact: [email protected]): Analysis of brain-wide task encoding during learning – Understanding how populations of neurons encode information is essential for deciphering brain function. Beyond single-neuron analyses, decoding task-related variables from the collective activity of neural populations allows us to quantify the type and amount of information represented in specific brain areas. This approach offers valuable insights into the neural mechanisms underlying goal-directed behavior. The aim of this project is to apply a range of decoding analyses – along with the appropriate controls – to neural population activity recorded during a novel learning task using a uniquely large dataset. Specifically, we seek to identify where and how the brain encodes newly learned stimulus–reward associations. We are looking for an outstanding and highly motivated student to help us in the data analysis. The project will enable the student to manipulate large datasets of spiking activity and implement advanced analysis methods. This project requires advanced programming skills (Python), experience with machine learning, statistics, data visualization and an interest for neuroscience and behavior.
Project 4 (please contact: [email protected]): Analysis of brain-wide learning dynamics using dimensionality reduction techniques – Understanding how populations of neurons encode information is essential for deciphering brain function. While single-neuron analyses provide important insights, examining the collective structure of neural population activity allows us to uncover low-dimensional patterns and subspaces that reflect underlying computations. Dimensionality reduction techniques help identify how neural representations evolve with learning, and how distinct task variables are organized across brain regions. The goal of this project is to apply a range of dimensionality reduction and subspace decomposition methods—such as PCA, demixed PCA, CEBRA, canonical correlation analysis—to neural population activity recorded during a novel learning task using a uniquely large dataset. We aim to uncover where and how newly learned stimulus–reward associations emerge in low-dimensional neural manifolds across brain areas. We are looking for an outstanding and highly motivated student to contribute to this analysis. The project will provide opportunities to work with large-scale spiking datasets and to implement advanced data analysis techniques. Strong programming skills (Python), experience with machine learning or dimensionality reduction methods (linear algebra), and a keen interest in neuroscience and behavior are essential.
Project 5 (please contact: [email protected]): Analysis of brain-wide neural interactions during learning – The mouse brain is a complex network of millions of neurons, each receiving and projecting signals to hundreds of other neurons. Recent advances in recording technologies allowed us to measure thousands of single neurons simultaneously during an associate learning task. Our unique large-scale dataset is amenable to the analysis of the concerted activity of many neurons in various brain regions during behaviour. The goal of this project is to quantify interactions at a brain-wide scale, and explore how learning may shape these interactions and/or how these inter-connected subpopulations respond during the learning task. The focus can be on single-neuron or population measures of interactions. We are looking for an outstanding and highly motivated student to help us in the data analysis. The project will enable the student to manipulate large datasets of spiking activity and implement advanced analysis methods. This project requires advanced programming skills (Python), experience with machine learning, signal processing, statistics, data visualization and an interest for neuroscience and behavior.
Project 6 (please contact: [email protected]): Analysis of brain-wide neuronal selectivity during learning – Understanding how individual neurons encode information is fundamental to deciphering brain function. Single-neuron measures of selectivity provide a mean to characterise how neurons respond to particular stimuli or task-related events, providing insights on the neural mechanisms underlying behaviour. The goal of this project is to quantify and model single-neuron measures of selectivity at a unique brain-wide scale, and explore how learning may shape this selectivity. We are looking for an outstanding and highly motivated student to help us in the data analysis. The project will enable the student to manipulate large datasets of spiking activity and implement advanced analysis methods. This project requires advanced programming skills (Python) experience with machine learning, statistics, data visualization and an interest for neuroscience and behavior.