Brain Modeling Algorithms
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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BrainTrace: model-agnostic linear-memory online learning for spiking networks
Online learning in spiking neural networks (SNNs) has long been bottlenecked by memory cost that scales linearly with simulation time. BrainTrace introduces a model-agnostic, eligibility-trace-based online learning algorithm that reduces memory to linear, and ships as a compiler that can be embedded in any SNN simulation. For the first time, biologically plausible local learning rules can run at large SNN scale — connecting the simulation and learning tracks of brain research. The algorithm is implemented as the BrainTrace module of the BrainX ecosystem.
Model-agnostic linear-memory online learning in spiking neural networks
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A differentiable approach to multi-scale brain modeling
Multi-scale brain models span cells, circuits, and regions, with a vast parameter space that frustrates conventional fitting. This work makes the entire multi-scale simulation differentiable, so models can be optimized end-to-end across scales via gradients — turning data → multi-scale model into a single learnable path and substantially improving fit to experimental data. The method is tightly coupled with the BrainX differentiable runtime, anchoring our differentiable-simulation stack.
A Differentiable Approach to Multi-scale Brain Modeling
These algorithms do not stay on paper — they instantiate directly as the whole-brain computational models in the next track, letting us run reproducible computational neuroscience experiments inside a virtual brain.