Bias-Induced Crossover in Absolute Capacity of Dense Associative Memory
Abstract
The absolute capacity of dense associative memory has mainly been analyzed for unbiased patterns. Here we examine the effect of bias in centered binary patterns under the Krotov-Hopfield single-site criterion $P_{\mathrm{error}}=1/N$, where $P_{\mathrm{error}}$ is the probability that a single-site flip lowers the energy of a stored pattern and $N$ is the number of neurons. Each pattern component takes $1-q$ with probability $q$ and $-q$ otherwise, where $0<q\le1/2$. For polynomial interactions ...
Description / Details
The absolute capacity of dense associative memory has mainly been analyzed for unbiased patterns. Here we examine the effect of bias in centered binary patterns under the Krotov-Hopfield single-site criterion , where is the probability that a single-site flip lowers the energy of a stored pattern and is the number of neurons. Each pattern component takes with probability and otherwise, where . For polynomial interactions of order , a signal-to-noise analysis gives an absolute capacity of order at . For fixed , however, the capacity is for even and for odd . For , both the unbiased and fixed-bias capacities remain . For , these different asymptotic forms imply a nonuniform large- limit near . Asymptotic matching predicts a bias-induced crossover in the region . The crossover originates from a bias-dependent crosstalk mean that reduces the stability of sites carrying the more frequent value . Computer simulations are compared with the finite-size conditioned-Gaussian predictions. An activity-dependent control potential that cancels the conditional crosstalk mean restores the capacity for fixed within the conditioned-Gaussian approximation.
Source: arXiv:2609.17477v1 - http://arxiv.org/abs/2609.17477v1 PDF: https://arxiv.org/pdf/2609.17477v1 Original Link: http://arxiv.org/abs/2609.17477v1
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Sep 16, 2026
Data Science
Machine Learning
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