most citedDynamical mean field approach to associative memory model with non-monotonic transfer functions

1 citations · 1 across the 4 of their papers we have counts for

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5 papers

cond-mat.dis-nn2026

DMFT analysis of Hopfield network with plasticity

Yoshinori Hara, Yoshiyuki Kabashima

We study a fully connected Hopfield-type associative memory network with online activity-dependent synaptic plasticity, where neural states and synaptic couplings coevolve during r…

cond-mat.stat-mech2026

Dynamical Regimes of Discrete Diffusion Models

Tomoei Takahashi, Takashi Takahashi, Yoshiyuki Kabashima

Diffusion models generate high-dimensional data such as images by learning a process that gradually removes noise from corrupted data. Recent studies have shown that the backward d…

cond-mat.stat-mech2026

Testing the Role of Diagonal Interactions in High-Order Hopfield Models via Dynamical Mean-Field Theory

Yuto Sumikawa, Yoshiyuki Kabashima

High-order extensions of the Hopfield model are known to exhibit dramatically enhanced storage capacity at equilibrium, while their dynamical retrieval properties remain less well…

cond-mat.dis-nn20261 cited

Dynamical mean field approach to associative memory model with non-monotonic transfer functions

Yoshiyuki Kabashima, Kazushi Mimura

The Hopfield associative memory model stores random patterns in synaptic couplings according to Hebb's rule and retrieves them through gradient descent on an energy function. This…

cond-mat.dis-nn2025

Dynamical Properties of Dense Associative Memory

Kazushi Mimura, Jun'ichi Takeuchi, Yuto Sumikawa +2

Dense associative memory, a fundamental instance of modern Hopfield networks, can store a large number of memory patterns as equilibrium states of recurrent networks. While the sta…