Local 2- and 3-cycles enhance RNN computational capacity for Boolean functions, predicted by structural statistics, while adding interneurons boosts large networks.
On graphical domination for threshold-linear networks with recurrent excitation and global inhibition.arXiv preprint arXiv:2510.05098, 2025
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Sequential chaotic oscillations arise in E-I threshold-linear networks under constant input, with transition order predictable from the graph when singleton fixed points are unstable and inhibition is strong.
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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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Sequential chaotic oscillations in excitatory-inhibitory threshold-linear networks
Sequential chaotic oscillations arise in E-I threshold-linear networks under constant input, with transition order predictable from the graph when singleton fixed points are unstable and inhibition is strong.