Research
We build multi-scale, high-fidelity, scalable brain-simulation platforms and computational models. From foundational software and parallel algorithms to modeling theory and computational models, we push end-to-end simulation from ion channels through cells, circuits, and whole-brain networks.
Our research advances along three mutually-supporting tracks: BrainX, our in-house software ecosystem, as the infrastructure; differentiable and learnable modeling algorithms as the methodology; whole-brain models of model organisms as the scientific testbed. Our long-term goal is a biologically realistic, functionally meaningful whole-brain model — applied to decoding brain function, studying disease mechanisms, and powering next-generation brain-inspired AI.
Three tracks
01
BrainX — our in-house open-source brain-simulation ecosystem, spanning ion channels to whole-brain networks, with native heterogeneous-hardware support and a clear path to hundred-billion-neuron whole-brain dynamics.
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02
Whole-brain simulation must simultaneously satisfy four goals often taken to be in tension: biological realism, computational efficiency, interpretability, and learning capacity. This track presents the algorithm system we develop to reconcile them, anchored by two representative results.
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03
Understanding brain function requires linking molecular, cellular, circuit, and systems-level data at whole-brain scale. We combine the BrainX software, our differentiable and learnable algorithms, and multi-modal neuroscience data to build whole-brain models of model organisms — serving as both scientific output and algorithm validation, and as the foundation for general brain models.
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