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A novel deep-learning-based computational method using object recognition to quantify visual context in naturalistic, multimodal stimuli demonstrates that a concept's perceived abstractness or concreteness dynamically depends on its visual context.

During awake Sharp wave-ripple memory reactivation, hippocampal-cortical representations for movement- versus immobility-associated experiences are reactivated separately.

The anterior cingulate cortex intermixes a precise spatial map with a cognitive map of the task, and spontaneously recalls multimodal information about unrealized choice outcomes during pauses in behavior after reinforcements.

Cross-modal stimuli are represented in the primary gustatory cortex according to their sensory identity, associability and predictive value.

The prediction of specific words is associated with distinct spatial and temporal patterns of neural activity within the left inferior and medial temporal regions before the predicted word is presented.

Enhanced hippocampal-cortical network communication during memory retrieval flexibly tracks the quality and content of memories for complex past events.

Real-time imaging of neural activity in behaving mice reveals that the two canonical types of neurons in the olfactory tubercle carry distinct information about learned odor cues, with D1 neurons representing stimulus valence and D2 neurons mainly representing stimulus identity.

The cortical grasping circuit separates but shares visual and motor processes to transform object attributes into appropriate hand movements.

The amyloid patterns overlap with the default-mode network, whereas the tau patterns overlap with distinct functional networks and are associated with a loss of anatomical connectivity and multiple cognitive functions.

Changes in the interpretation of specific scenes in a narrative trigger corresponding updates in the neural patterns evoked by those scenes in the default mode network.