Haberl, M. | Search bioRxiv

bioRxiv2025

Artificial hibernation uncovers distinct synaptic engram architecture for memory retention

The memory trace at the neuronal and synaptic levels remains controversial. Stable, larger spines are thought to support memory, but the high turnover of dendritic spines and the drifting of neuronal representations following memory formation suggest alternative possibilities. To elucidate a structural trace underlying memory retention, we utilize a mouse model of artificial hibernation. During hibernation, hippocampal neurons exhibited a substantial reduction in their activity and an extensive elimination of dendritic spines and synapses. Despite these changes, their memory and associated hippocampal neuronal representations are intact after arousal. We find that a subset of spines is maintained during hibernation. These spatially clustered engram-engram synapses are exclusively protected from elimination and characterized by synaptic contacts with multi-synaptic boutons. These findings suggest that synaptic engram architecture, rather than larger spines per se, is resilient to network remodeling and underlies long-term memory retention.

neuroscience↗

bioRxiv2018

CDeep3M - Plug-and-Play cloud based deep learning for image segmentation of light, electron and X-ray microscopy

As biological imaging datasets increase in size, deep neural networks are considered vital tools for efficient image segmentation. While a number of different network architectures have been developed for segmenting even the most challenging biological images, community access is still limited by the difficulty of setting up complex computational environments and processing pipelines, and the availability of compute resources. Here, we address these bottlenecks, providing a ready-to-use image segmentation solution for any lab, with a pre-configured, publicly available, cloud-based deep convolutional neural network on Amazon Web Services (AWS). We provide simple instructions for training and applying CDeep3M for segmentation of large and complex 2D and 3D microscopy datasets of diverse biomedical imaging modalities.

cell biology↗