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The specification of a very small number of progenitor cells with competence to adapt their neurogenic output to different probabilistic rules underlies the generation of distinct cortical cytoarchitectures.

Patients with DYT1 dystonia show aberrant risk-aversion in a simple decision-making task, in accordance with predictions of a reinforcement learning model of corticostriatal trial-and-error learning.

The cell cycle phosphatase CDC25B controls cell cycle progression and neurogenesis through independent pathways.

Emergent property inference, a novel machine learning methodology, learns distributions of neural circuit model parameters that produce computational properties and provides novel scientific insight through the quantification of the rich parametric structure it captures.

The dorsolateral prefrontal cortex integrates concurrent externally and internally generated predictions of task demand to guide information processing, while the medial prefrontal cortex corrects its prediction error based on actual task demand.

Attenuated anticipatory activity in ventromedial prefrontal cortex is modulated by dopamine D1 receptor density in nucleus accumbens, and accounts for impaired probabilistic reward learning in older adults.

A statistical model rescues multi-mapping reads with high accuracy and demonstrates their impact in all facets of the analysis of genome-wide high throughput conformation capture datasets.

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

Computational modeling predicts that sleep replay plays a protective role against catastrophic forgetting by revealing synaptic mechanisms allowing overlapping populations of neurons to store multiple interfering memories.

Keeping flexible adaptable representations of speech categories at different time scales allows the brain to maintain stable perception in the face of varying speech sound characteristics.