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Thermodynamic properties of extremely diluted symmetric Q-Ising neural networks

D. Bolle'·D. M. Carlucci·G. M. Shim
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Abstract

Using the replica-symmetric mean-field theory approach the thermodynamic and retrieval properties of extremely diluted {\it symmetric} $Q$-Ising neural networks are studied. In particular, capacity-gain parameter and capacity-temperature phase diagrams are derived for $Q=3, 4$ and $Q=\infty$. The zero-temperature results are compared with those obtained from a study of the dynamics of the model. Furthermore, the de Almeida-Thouless line is determined. Where appropriate, the difference with other $Q$-Ising architectures is outlined.

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D. Bolle', D. M. Carlucci, G. M. Shim. 2000-08-16. Thermodynamic properties of extremely diluted symmetric Q-Ising neural networks. https://doi.org/10.1088/0305-4470%2F33%2F37%2F302

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