Unbiased and automatic annotation using structured prediction framework with efficiently built data-driven atlases is more accurate than registration-based methods for cell identifications in dense images and enables fast whole-brain analysis.
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.
Analysis of human fMRI data reveal that intermediary areas within the fronto-parietal control network (FPCN) are critical for integrating control processing, cognitive ability, and amenability to neuromodulation.
Multi-modal structural data fusion questions the specificity of fMRI-behavior associations by providing strong evidence relating human brain structure to a wide range of behavioral measures previously associated to functional connectivity.
Previously uncharacterized long repeat sequences are associated with significant genome variation that can increase fitness and promote antifungal drug resistance in diverse isolates of Candida albicans.