MSc Thesis projects
If you are interested in doing your MSc thesis, there are a number of MSc thesis projects available. These projects involve either neural actvity analysis, video analysis or computational modelling of the brain.
Neural data analysis : We are interested in characterizing brain-wide changes in the neural activity during Parkinson's diseases (PD). To this end we have collected MEG data from healthy controls and patients with PD. This dataset is quite large in terms of number of MEG channels and duration of recording. Our preliminary analysis already shows some crucial differences in healthy controls and PD patients. There are still numerous opportunities to analyze this data to understand how interaction between different regions is altered during PD.
Required expertise (0ne or more of the following) : Machine learning tools, signal processing, stochastic processes
Collaboration : National MEG Facility , Daniel Lundqvist, Per Svenningsson(KS)
Analysis infant movements from videos : Infant movements are often useful in determining whether an infant might develop other disorders such as epilepsy. Clinicians routinely look at the infant's movements for this kind of diagnosis. We wish to make this more objective. And that requires that we have a data analysis pipeline to process infant videos (1) to track hands and legs using markerless tracking methods (eg deeplabcut) and (2) to analyze the tracking data and identify signatures of future disorders from hand/leg movement motives.
Required expertise (0ne or more of the following) : Machine learning tools for marker-less tracking, Unsupervised clustering methods
Collaboration : Heléne Sundelin (KS)
Video tracking and analysis of animal behavior : To better understand how neuronal activity is related to the animal behavior it is important that animals are operating in a relatively unconstrained environment. However, only recently machine learning and deep learning have provided useful tools to extract behavior from the video of animals. Therefore, new experiments are conducted in conditions where animals have a relatively unconstrained environment. But this means that the behavior becomes very complex and cannot be analyzed manually.
In this type of project we are interested in extracting repeatable behavioral motifs from videos of freely behaving animals. While behavior itself is very interesting as very little is known about animal behavior in unconstrained environment but the long-term goal is to identify the neural correlates of such complex behavioral motifs.
Required expertise (0ne or more of the following) : Machine learning, video analysis, mathematical modeling
Collaboration: Gilad Silberberg
Multi-scale models of biological neuronal networks : Understanding dynamical and information processing properties of biological neuronal networks is fundamental to understand the brain function. In my group we are interested in (1) isolating the role of various neuron and synapse weaknesses and (2) understanding how network structure affects network activity dynamics. Now more and more new data about the neurons, synapses and connectivity is becoming available. So we would like to integrate that in our models. Besides theoretical interest, we can adapt such models to understand mechanisms underlying brain diseases.
Required expertise (0ne or more of the following): Strong background in Physics, Maths or Electrical Engineering and motivation to ask and understand scientific questions (not just training a network).
Start date: As early as possible
Project duration: 3-4 Months
Contact Arvind Kumar ( )
More information about Arvind's research group: NeuroLogic
