User Group: Researcher

The course content includes research areas in radiation therapy, non-invasive imaging and tumor and radiation biology as well as machine learning, computer vision, basics for a better understanding, analysis and visualization of scientific data.

AI methods are already being used in cancer treatment, including for the evaluation of complex data or in therapy decisions. The knowledge acquired in the master’s profile can be used, for example, to optimize imaging for radiation planning, to contour organs and tumors using deep learning methods, or to better control radiation treatments. Graduates will also be able to contribute to the further development of individualized diagnostic and therapeutic procedures through AI in the future, both in research and in industry.

Researchers from the Faculty of Medicine and the Faculty of Mathematics and Natural Sciences at the University of Tübingen and the University Hospital Tübingen as well as from Cyber Valley will teach in this profile area. Cyber Valley is Europe’s largest research consortium in the field of AI and it connects partners from politics, science, industry and society. This ecosystem in southwestern Germany is continuously growing with additional multidisciplinary initiatives, making the Stuttgart-Tübingen region a hotspot for research and innovation in the fields of machine learning, robotics and computer vision.

In addition, the master’s profile is part of the AI in medicine initiative, in which the University of Tübingen, the Faculty of Medicine and the Max Planck Institutes, together with other numerous Cyber Valley partners, are advancing the use of AI in medicine and the life sciences.

Each process in nature happens at its own pace: neurons take milliseconds to signal to each other, demographic changes occur over years, and climate change is a matter of decades or even millennia. The period that a process typically needs to undergo a change is known as its timescale, and understanding the timescale is paramount for a deep understanding of the process itself.

How long does change typically take?

Statisticians make this idea of a timescale very precise. In the example of neural processes, the state of your brain right now is influenced by the state some time ago. The extent of this influence is captured by the temporal correlation. This correlation decreases over time: the longer the elapsing time, the weaker the dependency. The timescale of your neural processes is just this: a number that indicates how fast this correlation typically decreases and thus how fast the brain forgets its state.

In practice, determining the timescale from empirical data can be tricky. Typically, one would calculate correlations of the measured brain activity at different times; the decreasing dependency then indicates the timescale.

Recreating the cake to determine its sugar content

“Unfortunately, this method of determining timescales is flawed and can create misleading results,” explains Roxana Zeraati, researcher at the University of Tübingen and at the Max Planck Institute for Biological Cybernetics. “The problem is that the empirical data is always measured over finite, often short, time. Because of this, the average dependency between what happens at different points in time is systematically underestimated.” Zeraati stresses how important a correct measurement of timescales is: “A lot of conclusions in neuroscience rely on precise estimates of timescales. To give just one example, we think that aberrant timescales are linked to autism. But people never realized before that they might be estimating timescales incorrectly.”

This is why Zeraati and her collaborators came up with a novel idea to determine timescales: the scientists propose to generate new, artificial data from a computer model that match closely the empirical data. Anna Levina, assistant professor in Tübingen and Zeraati’s PhD advisor, explains the method with a simple analogy: “If chemically analyzing the sugar content in a cake your grandmother made is tricky – possibly because sugar from the frosting seeps into the cake and messes with your measurements – you can instead try to recreate the cake several times with different amounts of sugar, and the cake that comes closest to the original tells you how much sugar was in your grandmother’s, precisely because you know how much sugar you used for your cakes.”

Analyses of memory

The researchers subsequently tested the potential of their new method on data from neural recordings of the visual cortex from a previous study. “We wondered if spontaneous fluctuations of neural activity were ruled by a single timescale, or if maybe the process we observed was the sum of several processes with different timescales,” says Levina. “In that particular case, we saw that the process in the brain involved two different intrinsic timescales – something that never before has been reported.”

Zeraati adds: “In a similar way, we can analyze almost any intrinsic process in the brain, for example how neurons remember the past. This makes our new estimation method an incredibly valuable and powerful tool for neuroscientists and other researchers.”

Powered exoskeletons are usually not ready for immediate use but have to be adjusted by an expert, so that the different characteristics of the human body, the biomechanics of gait and the wearer’s preferences can be taken into account. Alternatively, this can be done by automatically evaluating quantifiable data such as metabolic rate or muscle activity in order to minimize energy consumption, for example. Or the wearer can be asked to repeatedly compare between different settings in order to find out which one is the most comfortable. However, the setting that leads to a minimum of energy consumption is not necessarily the most comfortable. Letting users choose between numerous settings takes time. Moreover, it does not reveal how the setting parameters interact with each other and affect the user experience.

The focus is on the user as a person