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A non-invasive imaging technique can translate scenes in your head into sentences. It could help to reveal how the brain interprets the world.

Neural mechanisms underlying reinforcement learning in naturalistic environments are not fully understood. Here authors show that reinforcement learning (RL) agents with hippocampal-like recurrence, u...

Spatial world models, representations that support flexible reasoning about spatial relations, are central to developing computational models that could operate in the physical world, but their precise mechanistic underpinnings are nuanced by the borrowing of underspecified or misguided accounts of human cognition. This paper revisits the simulation versus rendering dichotomy and draws on evidence from aphantasia to argue that fine-grained perceptual content is critical for model-based spatial reasoning. Drawing on recent research into the neural basis of visual awareness, we propose that spatial simulation and perceptual experience depend on shared representational geometries captured by higher-order indices of perceptual relations. We argue that recent developments in embodied AI support this claim, where rich perceptual details improve performance on physics-based world engagements. To this end, we call for the development of architectures capable of maintaining structured perceptual representations as a step toward spatial world modelling in AI.

Over 50 million people worldwide suffer from epilepsy, and many patients remain resistant to medication. Here, the authors hypothesized that targeting thalamic nuclei with precise anatomical...

Deep learning for neural decoding in brain-computer interfaces

CSHL Simons Fellow in NEUROSCIENCE Cold Spring Harbor Laboratory (CSHL) is seeking to fill a Cold Spring Harbor Laboratory Fellow position in the area of NEUROSCIENCE (experimental and/or computationa...