NeuralCPM learns a neural-network Hamiltonian for cellular Potts simulations from data, and reproduces self-organization dynamics beyond what analytical Hamiltonians achieve.
(2018) consist of 200 to 240 cells in 3D which amounts to about 8 cells along a diameter and about 40 cells in the cross-section
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Deep Neural Cellular Potts Models
NeuralCPM learns a neural-network Hamiltonian for cellular Potts simulations from data, and reproduces self-organization dynamics beyond what analytical Hamiltonians achieve.