3 papers
cs.CV2026
Rethinking Attention Locality in Spiking Transformers
Zeqi Zheng, Zizheng Zhu, Yuping Yan +3
Spiking Transformers provide a promising paradigm for efficient visual processing with spike-driven computation, yet their Softmax-free Spiking Self-Attention (SSA) struggles to es…
q-bio.NC2025
Self-motion as a structural prior for coherent and robust formation of cognitive maps
Yingchao Yu, Pengfei Sun, Yaochu Jin +7
Most computational accounts of cognitive maps assume that stability is achieved primarily through sensory anchoring, with self-motion contributing to incremental positional updates…
cs.NE2025
IP-RSNN: Bi-level Intrinsic Plasticity Enables Learning-to-learn in Recurrent Spiking Neural Networks
Yingchao Yu, Yaochu Jin, Kuangrong Hao +5
Learning-to-learn (L2L), defined as progressively faster learning across similar tasks, is fundamental to both neuroscience and artificial intelligence. However, its neural basis r…