Tianhao Chu

The paper trains RNNs with short-term plasticity to perform decision-making tasks, showing that evidence accumulates during activity-silent periods through synaptic efficacy changes.

This paper proposes that theta sweeps in grid and place cells accelerate cognitive map learning by broadcasting sensory information to unvisited regions, and presents a computational model to test this hypothesis.

This study shows that place and grid cells complement each other through reciprocal connections to achieve robust and efficient spatial representation in the brain.

The paper presents a computational model explaining alternating theta sweeps in the hippocampal circuit using firing rate adaptation and continuous attractor dynamics, validated against experimental data.

This paper uses recurrent neural networks to model path integration and reveals a hierarchical processing pathway involving band and grid cells.

This study identifies a neural mechanism involving firing rate adaptation that modulates hippocampal replay dynamics, showing correlations with theta sequence length and behavioral states.

This study shows that firing rate adaptation in a continuous attractor neural network explains theta phase precession and procession in hippocampal place cells.