Direction maps and pinwheel structures in MT emerge spontaneously when a spatiotemporal deep network is trained on videos with contrastive self-supervised learning and spatial regularization.
Journal of Physics C: Solid State Physics6(7), 1181–1203 (1973)
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Numerical study demonstrates controlled transport of Z4 parafermion edge states in a ladder model and quantifies the adiabatic speed limit under realistic conditions.
Numerical simulations of the triangular Majorana-Hubbard ladder reveal multiple symmetry-protected topological phases identified through entanglement spectrum degeneracies and adiabatic connections.
citing papers explorer
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Self-organized MT Direction Maps Emerge from Spatiotemporal Contrastive Optimization
Direction maps and pinwheel structures in MT emerge spontaneously when a spatiotemporal deep network is trained on videos with contrastive self-supervised learning and spatial regularization.
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Shuttling of $\mathbb{Z}_4$ parafermions in an electronic ladder model
Numerical study demonstrates controlled transport of Z4 parafermion edge states in a ladder model and quantifies the adiabatic speed limit under realistic conditions.
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Symmetry-Protected Topological Phases in the Triangular Majorana-Hubbard Ladder
Numerical simulations of the triangular Majorana-Hubbard ladder reveal multiple symmetry-protected topological phases identified through entanglement spectrum degeneracies and adiabatic connections.