Comprehensive dataset of high-level cognitive assessment in mouse models of neurodegeneration, accompanied by an open-access database/repository to change the paradigm of how cognitive studies in animal models can be shared/re-used.
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.
Leave-One-Trial-Out (LOTO) is a general, efficient and easily implementable approach for inferring trial-by-trial measures of computational model parameters in order to link these measures to neural mechanisms.
A biologically plausible learning rule allows recurrent neural networks to learn nontrivial tasks, using only sparse, delayed rewards, and the neural dynamics of trained networks exhibit complex dynamics observed in animal frontal cortices.
Regulatory success operates by goal-consistent increases and decreases of distinct attribute representations in generic neural hubs and in domain-specific brain regions, explaining when and why regulatory success generalizes across domains and contexts.