Ning Qian A

This paper trains neural networks to continuously update and control neural population activities using sparse time samples and a continuity equation loss, without requiring full time courses.

This paper introduces blur and retinal masks as self-supervision signals for vision transformers, showing improved robustness and generalization in visual representation learning.

The paper examines how receptive-field sizes change during remapping and shows they remain stable despite varying input sizes, supporting a mechanism for uniform visual updating.

The paper proposes a circuit model explaining transsaccadic space updating and mislocalization through population activity shifts and corollary discharge mechanisms.

This study identifies mechanisms by which neural networks overcome sensory-memory interference using input gating and tuning inversion, supported by neural data from macaques.

The paper investigates perisaccadic and attentional remapping of receptive fields in LIP and FEF, showing convergent and forward remapping mechanisms.