RESEARCH:
Advancing our understanding of how the brain encodes, stores, and retrieves information through networks of interconnected neurons represents a pivotal challenge in neuroscience. While tremendous progress has been made in examining the roles of network connectivity and synaptic plasticity, the precise ways in which neurons utilize these mechanisms to support learning and memory remain elusive. To investigate this complexity, researchers must develop tools that enable precise control of neuronal network topology, ensuring that measurable signals of learning and plasticity are not obscured by noise and variability.
Stephan Ihle’s research focuses on combining state-of-the-art electrophysiological methods with advanced AI-based techniques to create micro-physiological platforms for studying neuronal networks. By integrating bottom-up experimental approaches with computational modeling, his work seeks to disentangle the fundamental dynamics underlying neural learning processes. In this effort, he leverages controlled in-vitro systems that simplify and guide the refinement of computational models, validating them through high-resolution reconstruction of the network’s connectome. Ultimately, this integrated strategy will bridge experimental neuroscience and computational frameworks, driving new insights into the principles of memory and learning at the network level.
BIO:
Stephan J. Ihle is an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow at the University of Chicago. In his reasearch, he combines physics and neuroscience with machine learning. Stephan has received a Ph.D. in Electrical Engineering at the Swiss Federal Institute of Technology Zurich, Switzerland, where he was a Swiss Data Science Center fellow. During his PhD, he was advised by Prof. János Vörös in the Laboratory of Biosensors and Bioelectronics. As a Schmidt AI Fellow, Stephan’s goal is to develop machine learning techniques and promote the use of AI in science, specifically neuroscience.
