Decoding the brain with machine learning
The human brain is characterized by rich patterns of neuronal activity. Invasive and non-invasive measures of neuronal dynamics give unique windows into brain functions and their disruptions in brain disorders. Machine learning algorithms are powerful tools for analyzing large amounts of data collected from the healthy and diseased brain. They can uncover patterns of neuronal activity that are hidden to the human eye and can improve our understanding of brain function. In this talk I will present how machine learning can be applied in the field of neuroscience to analyze rich datasets of time-series and study brain functions in health and disease. I will start from clinical applications, and namely how machine learning can assist in disease prognostication and phenotyping, to how it can automate tedious work and contribute in hypothesis testing. Last, I will focus on open challenges related to bias, interpretability, and algorithmic deployment.
The human brain is characterized by rich patterns of neuronal activity. Invasive and non-invasive measures of neuronal dynamics give unique windows into brain functions and their disruptions in brain disorders. Machine learning algorithms are powerful tools for analyzing large amounts of data collected from the healthy and diseased brain. They can uncover patterns of neuronal activity that are hidden to the human eye and can improve our understanding of brain function. In this talk I will present how machine learning can be applied in the field of neuroscience to analyze rich datasets of time-series and study brain functions in health and disease. I will start from clinical applications, and namely how machine learning can assist in disease prognostication and phenotyping, to how it can automate tedious work and contribute in hypothesis testing. Last, I will focus on open challenges related to bias, interpretability, and algorithmic deployment.
Speakers
Athina Tzovara is an assistant professor at the Institute of Computer Science at the Faculty of Science and the Center for Experimental Neurology at the Faculty of Medicine, at the University of Bern, Switzerland. She received a diploma in electrical and computer engineering from the National Technical University of Athens, Greece in 2009, and a PhD in neuroscience from the University of Lausanne, Switzerland in 2012. She then moved to the University of Zurich as a postdoctoral researcher the University College London, UK as an honorary research associate, and the Helen Wills Neuroscience Institute, at the University of California Berkeley, USA. Her research combines modeling and machine learning techniques with invasive and non-invasive measures of neural activity in humans, to study the neural mechanisms that support human cognition in health and disease.
Join us on April 11th & 12th 2024
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