Browse our latest Neuroscience articles

A computational neural network model leverages a simple unsupervised learning principle to account for recent findings on when memories move apart (differentiate) or together (integrate) in the brain.

Computational modelling reveals variations in people’s preferences towards integrating choice attributes using an additive or multiplicative approach, which affected whether the presence of valuable distractors facilitate or impair decision making.

A computational model shows that preparation arises as an optimal control strategy in input-driven recurrent neural networks performing a delayed-reaching task.

The subthalamic nucleus contains distinct subpopulations that can support multifaceted roles for making decisions based on uncertain evidence.

Computational principles shed light on why movement is preceded by preparatory activity within the neural networks that control muscles.