A new dissimilarity measure that fits complexity-penalized diffeomorphisms from archetype dynamics to observed trajectories correctly identifies ring attractors, limit cycles, and working-memory motifs in simulated and RNN data, where DSA and SPE fail.
D., Ostrow, M., Zoltowski, D
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Dynamical Archetype Analysis: Autonomous Computation
A new dissimilarity measure that fits complexity-penalized diffeomorphisms from archetype dynamics to observed trajectories correctly identifies ring attractors, limit cycles, and working-memory motifs in simulated and RNN data, where DSA and SPE fail.