On the Phase Transition of Hopfield Networks — Another Monte Carlo Study

Daniel Volk ()
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Daniel Volk: Institute for Theoretical Physics, Cologne University, 50923 Köln, Germany

International Journal of Modern Physics C (IJMPC), 1998, vol. 09, issue 05, 693-700

Abstract: A Hopfield-type neural network has content addressable memory which emerges from its collective properties. I reinvestigate the controversial question of its critical storage capacity at zero temperature. To locate the discontinuous transition from good retrieval to bad retrieval in infinite systems the decreasing average quality of retrieved information is traced until it falls below a threshold. The cutoff points found for different system sizes are extrapolated towards infinity and yieldαc=0.143±0.002.

Keywords: Hopfield Model; Neural Networks; Phase Transition; Finite-Size Scaling; Multispin Coding (search for similar items in EconPapers)
Date: 1998
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DOI: 10.1142/S0129183198000595

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