Local 2- and 3-cycles enhance RNN computational capacity for Boolean functions, predicted by structural statistics, while adding interneurons boosts large networks.
Sterling, Philipp Schlegel, et al
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
ConnectomeBench2 supplies a unified multi-species benchmark of expert proofreading labels and shows a single Vision Transformer achieving human-level performance on split and merge error tasks while providing calibration and distribution-shift diagnostics.
Coarse wiring statistics set the dynamical regime while precise connections set activity geometry in a parameter-free model of the complete larval Drosophila connectome.
citing papers explorer
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Identifying structural design principles shaping the computational abilities of recurrent neural networks
Local 2- and 3-cycles enhance RNN computational capacity for Boolean functions, predicted by structural statistics, while adding interneurons boosts large networks.
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ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading
ConnectomeBench2 supplies a unified multi-species benchmark of expert proofreading labels and shows a single Vision Transformer achieving human-level performance on split and merge error tasks while providing calibration and distribution-shift diagnostics.
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Separating wiring-specific from statistical control of dynamics in a complete connectome
Coarse wiring statistics set the dynamical regime while precise connections set activity geometry in a parameter-free model of the complete larval Drosophila connectome.