Xue-Xin Wei

This paper develops a computational framework to model hippocampal replay dynamics using drift-diffusion processes, revealing insights into replay speed, randomness, and preplay events in rat hippocampal recordings.

This paper introduces split-trial analysis to quantify information-limiting noise in neural population codes, finding substantial noise in the mouse head direction system and consistent noise in macaque prefrontal cortex.

This study investigates the identifiability of Bayesian cognitive models from behavioral data, showing that key components can be reliably recovered under certain conditions.

The paper analyzes grid cell properties in the medial entorhinal cortex and finds small but robust variability within modules, which improves spatial encoding.

The paper shows that error distributions in behavioral tasks are non-Gaussian due to the geometry of the encoding manifold, and applies this to visual short-term memory tasks with two parameters.

This paper develops and tests neural networks that perform transitive inference, showing they generalize and exhibit behaviors similar to humans, while also revealing inconsistencies between human behavior and certain network solutions.

The paper shows that primary visual cortex maintains a luminance-invariant code despite retinal adaptation, through integration of parallel pathways at the geniculocortical synapse.

This paper introduces an unsupervised method to analyze neural tuning variability and discovers a new relationship governing orientation tuning variability in macaque visual cortex.

The paper presents a Bayesian theory that unifies different perceptual biases by decomposing them into three components and shows how they predict the direction of biases in perceptual estimation.

This study investigates how the thalamic head direction system adapts to environmental cues through dynamic population dynamics and memory mechanisms.

The paper introduces a zero-inflated gamma model for deconvolved calcium imaging traces, showing it outperforms simpler models in simulated and real neural data.

This paper introduces a model where border cells correct drift in grid cells through Hebbian plasticity, demonstrating a dynamic self-organization mechanism for robust path integration.