Asynchronous sequential updates in KLR Hopfield networks produce statistically indistinguishable trajectories from synchronous dynamics, achieve empirical capacities near P/N=30, and converge with event counts close to initial Hamming distance.
Self-organization and spectral mechanism of attractor landscapes in high- capacity kernel hopfield networks
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KLR Hopfield networks reach P/N storage of ~16 for random patterns and ~20 for structured data, with limits set by dynamical instability against noise rather than geometric separability per Cover's theorem.
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Efficient event-driven retrieval in high-capacity kernel Hopfield networks
Asynchronous sequential updates in KLR Hopfield networks produce statistically indistinguishable trajectories from synchronous dynamics, achieve empirical capacities near P/N=30, and converge with event counts close to initial Hamming distance.
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Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks
KLR Hopfield networks reach P/N storage of ~16 for random patterns and ~20 for structured data, with limits set by dynamical instability against noise rather than geometric separability per Cover's theorem.