Conditional diffusion models sample viable parameter manifolds whose geometry quantifies compensation, degeneracy, and effective constraints in Lorenz, Izhikevich, and reduced spiking-network models.
Minimizing informa- tion loss reduces spiking neuronal networks to differential equations.Journal of Computational Physics, 537:114117, 2025
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Diffusion learning reveals viable parameter manifolds and compensation geometry in biological dynamical systems
Conditional diffusion models sample viable parameter manifolds whose geometry quantifies compensation, degeneracy, and effective constraints in Lorenz, Izhikevich, and reduced spiking-network models.