Yul HR Kang

This study shows that predictive learning in recurrent neural networks generates Bayesian cognitive maps similar to hippocampal place cell activity, outperforming autoencoders.

The paper shows that spatial uncertainty, influenced by environmental geometry, explains navigational behavior and grid-cell properties through a Bayesian model.

The paper investigates how the brain processes two decisions about one object, finding parallel sensory acquisition but time-multiplexed evidence incorporation.

The paper introduces a fast heuristic algorithm to estimate time-varying decision thresholds from choice and reaction times without assuming their shape, outperforming conventional methods under time constraints.