Rapid, label-free, volumetric, and automated assessment of the immunological synapse dynamics is demonstrated by combining optical diffraction tomography and deep-learning-based segmentation, providing a new option for immunological research.
A deep learning-based pipeline was developed for extracting cellular signals flexibly from moving cells in 3D time lapse images, and it outperformed previous methods under different imaging conditions.
A multi-compartment spiking neural network model demonstrates that biologically feasible deep learning can be achieved if sensory inputs and higher-order feedback are received by different dendritic compartments.
DeepFly3D, a deep learning-based software, measures limb and appendage movements in tethered, behaving Drosophila and enables precise behavioral measurements during neural recordings, stimulation, and other biological experiments.
Drep-2 is the first representative of the evolutionary conserved CIDE-N protein family found at synapses and is required for associative learning by functionally intersecting with metabotropic signaling.