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Our teaching covers various aspects of the neurosciences and is part of the MSc Neuroscience program at the University of Freiburg and of the curriculum of the Bachelor and Master of Science Biology offered by the Faculty of Biology.

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Current Master Thesis projects, Bachelor Thesis projects and Research projects

Representing goal-oriented trajectories by hippocampal theta sequences and place cell directionality
(Master Thesis project / Bachelor Thesis project / Research project)

Hippocampal place fields have been shown to exhibit sequential firing activity in theta (4-12 Hz) cycles. These spike sequences depict running trajectories by connecting the representations of past, present and future locations. One potential mechanism is the recurrent connectivity of the hippocampus where cells with fixed connections fire the spikes one after the other. In addition, the firing rates of place cells were also found to be selective to certain heading directions. A recent study further revealed that such place field directionalities across the environment point to a movement’s goal location.

In this project, we are interested in how directionality could coordinate with theta sequences to represent prospective running trajectories which lead to the goal location. We assume that place cells receive directional input orienting to the goal location, which in turn activates and prioritizes pre-play of theta sequences and their trajectory representations pointing towards the goal area. This would allow flexible navigation based on past experiences or prior knowledge of the environment. To test our hypothesis, we will employ python programming to simulate a biological neural network of place cells and analyze the underlying spatiotemporal representation of the neural activity.



Comparison of informative neuronal activity patterns identified with linear and non-linear classification approaches (Research project)

In this project, the ability to visualize non-linear category boundaries in artificially generated data using linear classification methods will be explored. The most informative directions (MIDs) of binary classification tasks will be identified using methods developed in the lab in combination with state-of-the-art machine learning approaches. The results will then be compared to the ground-truth decision boundaries.  The methods will then be applied to extract experimentally recorded patterns of neural activity that carry most information about an animal’s behavior.



Identification of informative neuronal activity patterns using auto-encoder networks
(Master Thesis project)
– The spot for this project has already been occupied –

Interest on multivariate approaches for decoding has greatly increased over the past decade. Traditionally classification algorithms are employed for such tasks due to their state-of-the-art performance and straight forward implementations (support vector machines, deep neural networks). However, such algorithms usually come as a black-box as far as their decision making is concerned, providing us with no information regarding the most influential features or patterns within the data structure. Several methods from the field of explainable AI already exist which try to open this black-box (autoencoders, LIME, SHAP, DeepDIG) with a variety of different approaches. Within the lab, methods employing state of the art machine learning approaches from the field of computer vision have been developed to probe this black-box. In this project the performance of our methods will be evaluated using artificially generated datasets and compared to the already existing state-of-the-art alternatives. Furthermore, the effect of dimensionality and the structure of the data will be explored in this benchmarking protocol.



Modeling PFC burst sequences using artificial neural network
(Master Thesis project / Bachelor Thesis project / Research project)

Aim: Detecting burst sequences in artificial neural network and compare it with real data recorded from mouse prefrontal cortex (PFC).

In this project we aim to validate the results we observed from burst sequence analysis of calcium traces recorded from PFC by comparing them with the sequences generated by an artificial neural network. We use calcium traces recorded form mouse PFC, before, during and after performing a working memory task. Using burst sequence analysis, we observed significant changes in sequence rates of different conditions. In the next step, we try to test our method on data generated by an artificial neural network. Therefore, the main task in this project is to define a model (Preferably a recurrent neural network) that can mimic the neural activity in PFC and then implement the developed pipeline for sequence analysis to detect and quantify the pattern of bursts generated by the model. The results will help to better understand the neural basis of sequence generation in PFC.



Closed-loop components of hippocampal place field activity
(Bachelor Thesis project / Master Thesis project / Research project)

We will test recent state-of-the-art dimensional reduction method on the product space of hippocampal firing rates and behavioral tracking data from freely behaving rats. This analysis approach will elucidate the behavioral correlates of hippocampal network activity beyond spatial tuning of single cells. Students are expected to have basic programming skills in python and an interest in modern data science approaches.