Building on previous work (Drugowitsch et al., 2014), it is shown that decision-makers adjust their speed-accuracy trade-off flexibly from one moment to the next, based on evidence available from multiple sensory modalities, in order to nearly maximize their reward rate.

Single-cell experiments and mathematical modelling show that cellular signalling networks are vulnerable to trade-offs in speed versus accuracy, but that these vulnerabilities can be overcome by distributing the two tasks to different, although interacting, subnetworks.

During perceptual decision making, maximizing total reward in the long term requires trading reward in the short term for a faster improvement in perceptual representations.

To establish a trade-off between the speed and accuracy of a decision, neurons in lateral intraparietal cortex combine evidence bearing on the decision with a signal that incorporates the cost of time into the decision-making process.

Single-peaked tuning curves found in early sensory areas are more optimized for quick decoding than accuracy, while multi-peaked tuning curves (e.g. grid cells) give higher accuracy but only at longer time scales.

Dynamic changes in activity and connectivity of the human subthalamic nucleus reflect whether a decision will be made in haste or with caution.

A multidisciplinary approach sheds light on the fundamental mechanisms underlying social belief communication under uncertainty.

A new model is presented to simultaneously study the latent cognitive processes underlying decision making and instrumental learning.

A deep neural network trained on semi-synthetic data learns to quickly track and identify neurons in Caenorhabditis elegans.

BrainPy presents a general-purpose programming framework that enables efficient implementation, simulation, training, and analysis of brain dynamics models across multiple organization scales, ultimately facilitating the understanding of the complex neural mechanisms underlying brain functions.