Brain Activity Analysis, Modeling & Behavior Study

Our research integrates two lines of inquiry. Anchored in the analysis of population activity and dynamics in brain-inspired neural networks, it moves in two complementary directions:

developing a unified mathematical framework to reveal how structural connectivity shapes neural responses and network dynamic;
leveraging this framework to identify the key structural features and dynamic mechanisms that support adaptive, flexible behaviors, and enable Multi-Task Learning.

Ultimately, our goal is to bridge connectivity–dynamics–behavior across scales, from microscopic to large-scale brain networks, investigating how the connectome and other biological constraints enable complex cognitive functions and behavior.

Our working styles

Biologically‑constrained network connectivity – analytical & computational modeling of connectivity motifs (pairwise & triplet) and their impact on global dynamics.
Neuronal computation & cognition – developing interpretable recurrent neural networks (RNNs) that mimic animal behavior and cognitive functions.
Interdisciplinary synergy – blending mathematics, physics, neuroscience, and AI to build theory‑driven, data‑constrained models.

Current Open Positions

We are not currently recruiting PhD students or postdocs, but please check back for future opportunities.