Wolfgang Losert
Wolfgang Losert
Graduate Program Affiliations
- Applied Mathematics & Statistics, and Scientific Computation (AMSC)
- Biological Sciences (BISI): Molecular and Cell Biology (MOCB)
- Biophysics (BIPH)
- Chemical Physics (CHPH)
- Physics (PHYS)
Research Interests
The research of my team is motivated by the vision that quantitative data analytics, models, and machine learning will yield fundamental insights into the behavior of living systems, and will allow us to translate basic insights into novel approaches to control living systems. My group focuses on the use of physical signals (light, EM fields, sound, and texture) to restore or enhance the behavior cell groups. The group behaviors of particular interest are the collective motion of epithelial cells, important for development and wound healing, and the collective activity of brain cells, which are central to information processing and cognitive functions. centers on elucidating the physical and statistical properties of living systems from an excitable systems perspective.
Biosensing with excitability: We discovered that biomechanical waves can act as primary sensors of the physical environment of a cell, in particular electric fields and texture (Yang et al PNAS, 2023). Our finding also provides a new perspective on the biological impact of elongate mineral fibers (such as asbestos) (Gu et al, Environmental Research, 2023).
Biocomputing: My team aims to push boundaries at the convergence of neuroscience and AI by investigating the computing characteristics of living neural networks. Please see recent publications and preprints for the current status of this work. Work supported by ARL, AFOSR, and other sponsors.
Data Science in Physics: Data Science and Machine Learning are emerging as powerful drivers of innovation across all areas of physics including the physics of living systems. I served on the inaugural leadership team that started a new APS Group on Data Science (GDS) and served as chair of GDS in 2021-2022. I was part of a team that won an APS innovation award (PI: William Ratcliff, NIST) to start a Data Science Education Community of Practice (DSECOP).
Latest Papers
Characterizing learning in spiking neural networks with artificial astrocytes
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Neurocomputing
Author(s): Christopher S. Yang, Sylvester J. Gates, Dulara De Zoysa, et. al
UMD Author(s): Wolfgang Losert
Rhythmic sharing: A bioinspired paradigm for zero-shot adaptive learning in neural networks
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Physical Review Research
Author(s): Hoony Kang, Wolfgang Losert
UMD Author(s): Wolfgang Losert
Supramolecular aggregation of aquaporin-4 shapes astrocyte collective migration and mechanics
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Scientific Reports
Author(s): Barbara Barile, Nicholas John Mennona, Maria Grazia Mola, et. al
UMD Author(s): Wolfgang Losert
Localized ROS Generation with UV light in Differentiating Human Neural Progenitor Cells
Author(s): Sylvester J. Gates, III, Wolfgang Losert
UMD Author(s): Sylvester Gates, Wolfgang Losert
Collective learning in living neural networks facilitated by random contextual photostimulation
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Physical Review E
Author(s): Dulara De Zoysa, Sylvester J. Gates III, Anna M. Emenheiser, et. al
UMD Author(s): Sylvester Gates, Wolfgang Losert
Nascent actin dynamics and the disruption of calcium dynamics by actin arrest in developing neural cell networks
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Communications Biology
UMD Author(s): Kan Cao, Sylvester Gates, Wolfgang Losert
Nanotopographic Control of Actin Waves and Growth Cone Navigation in Developing Neurons
Author(s): Spandan Pathak, Kate M. O’Neill, Emily K. Robinson, et. al
UMD Author(s): Wolfgang Losert
Collective Learning in Living Neural Networks Facilitated by Contextual Background Photostimulation
Author(s): Dulara De Zoysa, Sylvester J. Gates, III, Anna M. Emenheiser, et. al
UMD Author(s): Sylvester Gates, Wolfgang Losert
Suppressing collective cell motion with bidirectional guidance cues
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Physical Review E
Author(s): Abby L. Bull, Molly Mosher, Paula Rodriguez, et. al
UMD Author(s): Wolfgang Losert
A comparative roadmap of PIWI-interacting RNAs across seven species reveals insights into de novo piRNA-precursor formation in mammals
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Other
Author(s): Parthena Konstantinidou, Zuzana Loubalova, Franziska Ahrend, et. al
UMD Author(s): Wolfgang Losert


