The detection of chemical synapses in 3-dimensional electron microscopy data has been automated such that synapses in large-scale connectivity maps of the cerebral cortex, connectomes, can be charted without the need for human interaction.

Emotional learning retroactively promotes memory integration for previously neutral events into episodic memory to foster future event predictions, through rapidly stimulating trial-specific reactivation of overlapping memory traces and reorganization of associated memories among the amygdala, hippocampal, and neocortical circuits.

A series of experiments shows how muscle fatigue impairs motor-skill learning on subsequent practice days beyond its effects on task execution.

Combined pre- and postsynaptically expressed long-term plasticity of neuronal connections improves sensory discrimination, and enables rapid relearning of previously encountered information.

Through mathematical analysis and computer simulations, the neuronal underpinnings of feasible models of sequence learning are delineated.

Individual differences in generalization of aversive value (but not safety information) during human active avoidance learning specifically predict experience of anxiety and intrusive thoughts.

When a common error drives parallel implicit and explicit learning systems, increases in the explicit response will suppress implicit adaptation.

A comparison of different bioimage analysis pipelines reveals how deep learning can be used for automatized and reliable analysis of fluorescent features in biological datasets.

During learning, human neural codes for experienced reward probability map onto the same mental number line for symbolic numbers.

A domain-general structure learning mechanism, supported by anterior insula, moves beyond explicit category labels and dyadic similarity as the sole inputs to social group representations and predicts ally-choice behavior.