Distinct computational levels of uncertainty compel agents to gain information about hidden states in the world (‘active inference’) or optimise their ‘world model’ as such (‘active learning’).
The cells in our inner ear commonly believed to provide fast mechanical feedback are too sluggish to follow the vibrations evoked by high-frequency sounds.
Gaining genetic control over neural modules that drive the grooming of each Drosophila body part reveals how mechanisms for selecting among competing behavioral choices are used to generate sequences of actions.
An unbiased genome-wide human forward genetic screen identifies the vacuolar ATPase complex and assembly factors as regulators of HIF stability through their actions on intracellular iron metabolism.
Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics.
Single-cell RNA-seq analysis reveals an increased complexity of mosquito hemocytes, enabling the functional and molecular characterization of immune cell sub-populations in Anopheles gambiae.
The capacity for symbiosis between photosynthetic microalgae and early diverging lineages fungi was demonstrated with microscopy and stable isotope exchange of carbon and nitrogen.
Five mouse models of autism show deficits in delay eyeblink conditioning, a form of split-second sensory learning that involves the cerebellum, a frequent site of disruption in autistic brains.
Cooperation theory and a novel synthetic infection system provides a mechanistic understanding of why a seemingly successful disease management strategy can have devastating consequences for infected hosts.
