Yunzhe Liu

This study identifies brain-wide activation patterns during sleep rhythms using EEG and fMRI, showing thalamus and hippocampus involvement in memory processes.

This study shows that hippocampal ripples prioritize high-value, unvisited options during model-based learning by coordinating with the lateral frontopolar cortex.

The paper shows that hippocampal ripples reduce medial prefrontal cortex constraints, supporting creative thinking during idea generation.

The paper shows that replay-DMN coordination develops with age, predicting grid-like representations in the entorhinal cortex during memory formation.

The paper shows that hippocampal ripples during rest predict the emergence of grid-like codes in the brain for inferring novel relationships in a 2D conceptual space.

The paper shows that replay during rest integrates fragmented experiences into a coherent cognitive map, reducing the need for online computation during inference.

The paper shows that non-spatial grid-like neural codes in the entorhinal cortex develop with age and correlate with inferential reasoning and intelligence.

The paper introduces Du-IN-v2, a pre-training framework for speech decoding from intracranial neural signals, achieving state-of-the-art performance using region-level token embeddings and discrete codex-guided mask modeling.

The paper develops a neural network model, Sleep Interpreter (SI), to decode memory replay from sleep EEG and shows it can accurately detect reactivation during sleep that correlates with memory performance.

This study uses EEG-fMRI to examine brain-wide activation during neural replay, finding correlations between replay strength and hippocampal activity, medial prefrontal cortex activity, and connectivity to the default mode network.

This study shows that visual sequence exposure induces non-feature-specific elevated responses and feature-specific backward replay in the human brain, suggesting distinct functional roles in processing and learning sequences.

This study uses EEG to investigate offline learning mechanisms, finding that backward replay strength correlates with trait anxiety and reward preference.

The paper shows that replay of reward and punishment paths during planning predicts approach and avoidance decisions, with replay of punishing paths influencing irrational choices in anxious individuals.

The paper investigates distinct neural replay signatures during decision-making and memory preservation in humans using MEG and multivariate analysis.

This paper introduces STRM, a method for analyzing M/EEG data to infer spatiotemporal activity patterns and improve predictive accuracy in neuroscience tasks.

The paper shows that aversive learning in humans involves hippocampal reactivation and sequential replay of task states, with aversive outcomes enhancing reverse replay.

The paper shows that visual understanding involves relational and compositional representations in the prefrontal-hippocampal circuit, with replay sequences forming hypotheses about scene configurations.

The paper shows replay bursts occur during activation of the default mode and parietal alpha networks, coordinating information flow between cortical areas.

The paper shows that reverse sequential replay supports non-local reinforcement learning in humans and is prioritized based on future utility.

The paper shows that successful retrieval of episodic memories involves compressed sequential replay of memory elements, which direction depends on task goals.

This paper introduces TDLM, a toolkit for analyzing neural activity sequences, showing its ability to detect human replay during rest using MEG data and parallels to rodent hippocampal replay.