Johann Georg Danzl

The paper introduces niiv, a self-supervised method for isotropic reconstruction of 3D microscopy data, achieving faster and higher-quality results than existing methods.

This paper develops LICONN, a light-microscopy based method for synapse-level connectomic reconstruction of brain tissue using hydrogel expansion and deep learning.

This paper introduces an image-based 3D active stabilization method for super-resolution microscopy, achieving ~1 nm stability without requiring fiducial markers or complex hardware.

This study develops and applies expansion microscopy to Arabidopsis root tissues, achieving improved resolution and visualizing subcellular structures.

This study identifies unique connectivity and synaptic rules in human hippocampal CA3 circuits that enhance associative memory capacity compared to rodents.

This paper introduces a method for saturated reconstruction of living brain tissue using advanced imaging and deep learning to achieve high-resolution, volumetric analysis of neural structures over time.

The paper introduces CATS, a method to map brain tissue architecture across scales, demonstrating its application to hippocampal mossy fiber circuitry.

This study identifies a functional role for entorhinal layer 6b subplate neurons in spatial learning and memory by showing their connectivity and impact on hippocampal activity.

This study shows that Cul3 mutations in mice cause ASD-like behaviors and neuronal migration defects during brain development, but not in adults.

This study compares synaptic function in mouse cerebellar slices prepared at ice-cold and physiological temperatures, finding that physiological temperature preserves synaptic structure and function better.