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.
Experimental determination of residue contacts from mutational data allows model discrimination and identification of in vivo functional conformations of proteins.
A stochastic model of locomotory control in C. elegans based on an extensive new set of tracking data can explain and predict effects of ablations and mutations on behavior.
Spontaneous theta oscillations and interneuron-specific phase preferences emerge spontaneously in a full-scale model of the isolated hippocampal CA1 subfield, corroborating and extending recent experimental findings.
A new agent-based model enables predicting how coral species richness and functional diversity affect the functioning and resilience of coral reef ecosystems.
The BB model explains spatial cognition in terms of interactions between specific neuronal populations, providing a common computational framework for the human neuropsychological and in vivo animal electrophysiological literatures.
Calcium imaging is used to construct a model of Caenorhabditis elegans nervous system dynamics capable of predicting future behavioral switches on a trial by trial basis and across individual animals.
Osteoclasts emerging in inflammation are heterogeneous and encompass a strongly inflammatory subset with a high resorbing activity and an immune suppressive subset that are able to control each other.
When mismatch repair is compromised heterozygous loss of Pol ε proofreading is sufficient to drive a subset of the observed clinical characteristics of Pol ε tumors.
