BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Sabre//Sabre VObject 4.5.8//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Zurich X-LIC-LOCATION:Europe/Zurich TZURL:http://tzurl.org/zoneinfo/Europe/Zurich BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19810329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19961027T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:news494@dbe.unibas.ch DTSTAMP;TZID=Europe/Zurich:20260402T130719 DTSTART;TZID=Europe/Zurich:20260409T163000 SUMMARY:Machine Learning for Optical Microscopy in Neuroscience DESCRIPTION:AbstractOptical microscopy is widely used to investigate biolog ical samples in vivo. However\, microscopic imaging in living tissue—suc h as the brain—faces multiple challenges. Samples are typically non-tran sparent and do not lie within a single focal plane\; they scatter light\, are three-dimensional\, and often not stationary at the micrometer scale.M achine learning offers novel ways to address these longstanding challenges . I will discuss recent work from our group on correcting aberrations and scattering using different approaches\, including neural networks and diff erentiable physics. I will also present tomographic imaging methods for op tical microscopy based on Bessel beam —line-shaped focal spots that exte nd across multiple focal planes. \\r\\nBiosketchJohannes Seelig studied p hysics at University of Basel with a minor in molecular biology from 1997- 2002. He obtained his PhD in the field of single molecule microscopy from ETH Zurich in 2006. He was a postdoctoral associate (2007-11) and later a research specialist (2011- 2015) at Janelia Research Campus where he worke d on neural circuits in Drosophila. From 2016-2025 he was “free-floating ” Max Planck Research Group Leader at the Max Planck Institute for Neuro biology of Behavior – caesar. X-ALT-DESC:

Abstract
Optical microscopy is widely u sed to investigate biological samples in vivo. However\, microscopic imagi ng in living tissue—such as the brain—faces multiple challenges. Sampl es are typically non-transparent and do not lie within a single focal plan e\; they scatter light\, are three-dimensional\, and often not stationary at the micrometer scale.
Machine learning offers novel ways to addres s these longstanding challenges. I will discuss recent work from our group on correcting aberrations and scattering using different approaches\, inc luding neural networks and differentiable physics. I will also present tom ographic imaging methods for optical microscopy based on Bessel beam —li ne-shaped focal spots that extend across multiple focal planes.
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Biosketch
Johannes Seelig studied physics at University of Basel with a minor in molecular biology from 1997-2002. He obtained his PhD in the field of single molecule microscopy from ETH Zu rich in 2006. He was a postdoctoral associate (2007-11) and later a resear ch specialist (2011- 2015) at Janelia Research Campus where he worked on n eural circuits in Drosophila. From 2016-2025 he was “free-floating” Ma x Planck Research Group Leader at the Max Planck Institute for Neurobiolog y of Behavior – caesar.

DTEND;TZID=Europe/Zurich:20260409T173000 END:VEVENT END:VCALENDAR