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State sampling dependence of the Hopfield network inference

By Haiping Huang


The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations. We present the system in the glassy phase with low temperature and high memory load. We find that the inference error is very sensitive to the form of state sampling. When a single state is sampled to compute magnetizations and correlations, the inference error is almost indistinguishable irrespective of the sampled state. However, the error can be greatly reduced if the data is collected with state transitions. Our result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.Comment: 4 pages, 1 figure, further discussions added and relevant references adde

Topics: Condensed Matter - Disordered Systems and Neural Networks, Quantitative Biology - Neurons and Cognition
Year: 2011
DOI identifier: 10.1088/0253-6102/57/1/27
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