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We study spin glasses on random lattices with finite connectivity. In the infinite connectivity limit they reduce to the Sherrington Kirkpatrick model. In this paper we investigate the expansion around the high connectivity limit. Within…

Disordered Systems and Neural Networks · Physics 2009-11-07 Giorgio Parisi , Francesca Tria

We discuss the physics of crystals with small polydispersity near the jamming transition point. For this purpose, we introduce an effective single-particle model taking into account the nearest neighbor structure of crystals. The model can…

Disordered Systems and Neural Networks · Physics 2020-08-19 Harukuni Ikeda

We obtain an exact analytic expression for the average distribution, in the thermodynamic limit, of overlaps between two copies of the same random energy model (REM) at different temperatures. We quantify the non-self averaging effects and…

Disordered Systems and Neural Networks · Physics 2021-02-03 Bernard Derrida , Peter Mottishaw

The phenomenon of PT (parity- and time-reversal) symmetry breaking is conventionally associated with a change in the complex mode spectrum of a non-Hermitian system that marks a transition from a purely oscillatory to an exponentially…

It is shown that a Hopfield recurrent neural network exhibits a scaling regime, whose specific exponents depend on the number of parcels used and the decay length of the coupling strength. This scaling regime recovers the picture introduced…

Disordered Systems and Neural Networks · Physics 2025-02-17 Giorgio Gosti , Sauro Succi , Giancarlo Ruocco

The Sherrington-Kirkpatrick spin-glass model used the replica symmetry method to find the phase transition of the system. In 1979-1980, Parisi proposed a solution based on replica symmetry breaking (RSB), which allowed him to identify the…

Disordered Systems and Neural Networks · Physics 2024-11-08 Ali Talebi , Mahsa Bagherikalhor , Behrouz Askari , G. Reza Jafari

Restricted Boltzmann Machines are described by the Gibbs measure of a bipartite spin glass, which in turn corresponds to the one of a generalised Hopfield network. This equivalence allows us to characterise the state of these systems in…

Disordered Systems and Neural Networks · Physics 2018-02-28 Adriano Barra , Giuseppe Genovese , Peter Sollich , Daniele Tantari

Spontaneous symmetry breaking in statistical mechanics primarily occurs during phase transitions at the thermodynamic limit where the Hamiltonian preserves inversion symmetry, yet the low-temperature free energy exhibits reduced symmetry.…

Computation and Language · Computer Science 2026-03-02 Shalom Rosner , Ronit D. Gross , Ella Koresh , Ido Kanter

The analogue of the paramagnetic to spin-glass phase transition in disordered magnetic systems, leading to the phenomenon of replica symmetry breaking, has been recently demonstrated in a two-dimensional random laser consisting of an…

We determine the low-energy spectrum and Parisi replica symmetry breaking function for the spin glass phase of the quantum Ising model with infinite-range random exchange interactions and transverse and longitudinal ($h$) fields. We show…

Disordered Systems and Neural Networks · Physics 2024-04-18 Maria Tikhanovskaya , Subir Sachdev , Rhine Samajdar

We study replica symmetry breaking (RSB) for multi-entropy in the random-tensor-network (RTN) domain-wall spin model. Our main result is that, within this framework, multi-entropy has a structural obstruction to RSB for any R\'enyi index…

High Energy Physics - Theory · Physics 2026-04-22 Sriram Akella , Norihiro Iizuka

We study the random energy model with a hierarchical structure known as the generalized random energy model (GREM). In contrast to the original analysis by the microcanonical ensemble formalism, we investigate the GREM by the canonical…

Disordered Systems and Neural Networks · Physics 2010-11-16 Tomoyuki Obuchi , Kazutaka Takahashi , Koujin Takeda

In certain mean field models for spin glasses there occurs a one step replica symmetry breaking pattern. As an example of general $1/N$-corrections in such systems, the fluctuations in the internal energy are calculated. For this specific…

Condensed Matter · Physics 2009-10-28 Th. M. Nieuwenhuizen

A large number of neural network models of associative memory have been proposed in the literature. These include the classical Hopfield networks (HNs), sparse distributed memories (SDMs), and more recently the modern continuous Hopfield…

Neural and Evolutionary Computing · Computer Science 2022-06-20 Beren Millidge , Tommaso Salvatori , Yuhang Song , Thomas Lukasiewicz , Rafal Bogacz

For the brain to recognize local orientations within images, neurons must spontaneously break the translation and rotation symmetry of their response functions -- an archetypal example of unsupervised learning. The dominant framework for…

Neurons and Cognition · Quantitative Biology 2021-09-07 Francesco Fumarola , Bettina Hein , Kenneth D. Miller

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 conventional setting, where neurons are assumed to have…

Disordered Systems and Neural Networks · Physics 2026-01-23 Yoshiyuki Kabashima , Kazushi Mimura

We discuss mean field theory of glasses without quenched disorder focusing on the justification of the replica approach to thermodynamics. We emphasize the assumptions implicit in this method and discuss how they can be verified. The…

Disordered Systems and Neural Networks · Physics 2009-10-31 L. B. Ioffe , A. V. Lopatin

We describe the effect of replica symmetry breaking in the field distribution function P(h) of the T=0 SK-model as the difference between a split Gaussian and the first excited state $\psi_1$ of a weakly anharmonic oscillator with…

Disordered Systems and Neural Networks · Physics 2015-05-28 Reinhold Oppermann , Hannes Schenck

We study the expectation value of the logarithm of the partition function of large binary-to-binary lattice-gas Restricted Boltzmann Machines (RBMs) within a replica-symmetric ansatz, averaging over the disorder represented by the…

Disordered Systems and Neural Networks · Physics 2023-01-25 David C. Hoyle

The gap between the huge volumes of data needed to train artificial neural networks and the relatively small amount of data needed by their biological counterparts is a central puzzle in machine learning. Here, inspired by biological…

Disordered Systems and Neural Networks · Physics 2022-04-19 Miriam Aquaro , Francesco Alemanno , Ido Kanter , Fabrizio Durante , Elena Agliari , Adriano Barra
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