Storing infinite numbers of patterns in a spin-glass model of neural networks.¶
Amit, D. J., Gutfreund, H., & Sompolinsky, H. (1985). Storing infinite numbers of patterns in a spin-glass model of neural networks. Physical Review Letters, 55(14), 1530-1533.
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Primes¶
- Associative Memory
- It will tolerate partial cues and noise, because any sufficient fragment lies in the basin of attraction of the stored item and converges to it; it will support graceful, similarity-graded recall rather than all-or-nothing lookup; and it will have a bounded capacity beyond which stored patterns interfere and recall degrades — a property quantified for the Hopfield model by Amit, Gutfreund, and Sompolinsky (1985), who derived the critical storage ratio above which the retrieval states are destroyed.
This sourceDerives the critical storage ratio (α_c ≈ 0.14) for the Hopfield model above which the patterns crowd, interfere, and the retrieval states are destroyed. SUPPORTS marker 135.
- It will tolerate partial cues and noise, because any sufficient fragment lies in the basin of attraction of the stored item and converges to it; it will support graceful, similarity-graded recall rather than all-or-nothing lookup; and it will have a bounded capacity beyond which stored patterns interfere and recall degrades — a property quantified for the Hopfield model by Amit, Gutfreund, and Sompolinsky (1985), who derived the critical storage ratio above which the retrieval states are destroyed.
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Registry ID ref:012ed3200908 · see in the full table