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.
fMRI evidence for off-task replay predicts subsequent replanning behavior in humans, suggesting that learning from simulated experience during replay helps update past policies in reinforcement learning.
Katja Kornysheva is a Sir Henry Wellcome Postdoctoral Fellow at the Institute of Cognitive Neuroscience, University College London and the Department of Neuroscience, University Erasmus Medical Centre.
Mathematical modeling suggests that grid cells in the rodent brain use fundamental principles of number theory to maximize the efficiency of spatial mapping, enabling animals to accurately encode their location with as few neurons as possible.
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.
Model-based analyses of human behaviour and neural activity show that representations of concurrent task-sets emerge by merging together representations of individual stimulus-response associations that occur in temporal proximity.
The momentary levels of local cortical desynchronization and pupil-linked arousal pose dissociable influences not only on the processing of sensory information but also on human perceptual performance.