Psychophysical experiments and computational modeling demonstrate the importance of transient, instead of sustained, channels for the integration of audiovisual signals.

The dynamical solutions exhibited by task-optimized recurrent neural networks, and their similarity to prefrontal cortex dynamics, depends strongly on the strength of neural noise and metabolic cost imposed during training.

CausalXtract is a computational tool to discover causal effects, beyond classical Granger and Schreiber causality approaches, from live-cell imaging data for a range of fundamental and translational research applications.