3 papers
q-bio.NC2025
Imaging the Topology of Dynamic Brain Connectivity
Peilin He, Tananun Songdechakraiwut
Functional brain connectivity changes dynamically over time, making its representation challenging for learning on non-Euclidean data. We present a framework that encodes dynamic f…
cs.NE2025
Connectome-Guided Automatic Learning Rates for Deep Networks
Peilin He, Tananun Songdechakraiwut
The human brain is highly adaptive: its functional connectivity reconfigures on multiple timescales during cognition and learning, enabling flexible information processing. By cont…
q-bio.NC2025
Data-Efficient Neural Training with Dynamic Connectomes
Yutong Wu, Peilin He, Tananun Songdechakraiwut
The study of dynamic functional connectomes has provided valuable insights into how patterns of brain activity change over time. Neural networks process information through artific…