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Recent studies have shown how spiking networks can learn complex functionality through error-correcting plasticity, but the resulting structures and dynamics remain poorly studied. To elucidate how these models may link to observed dynamics…

Neurons and Cognition · Quantitative Biology 2025-08-19 Jonas Oberste-Frielinghaus , Anno C. Kurth , Julian Göltz , Laura Kriener , Junji Ito , Mihai A. Petrovici , Sonja Grün

In the last years we have proposed the use of the mechanism of spontaneous symmetry breaking with the purpose of generating perfect quadrature squeezing. Here we review previous work dealing with spatial (translational and rotational)…

Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose a learning rule that takes inspiration from neuroscience and…

Neural and Evolutionary Computing · Computer Science 2021-10-27 Bernd Illing , Jean Ventura , Guillaume Bellec , Wulfram Gerstner

Selection rules are often considered a hallmark of symmetry. When a symmetry is broken, e.g., by an external perturbation, the system exhibits selection rule deviations which are often analyzed by perturbation theory. Here, we employ…

Optics · Physics 2022-04-06 Matan Even Tzur , Ofer Neufeld , Avner Fleischer , Oren Cohen

One common way to define spontaneous symmetry breaking involves explicit symmetry breaking. This definition can be used in any approach to Effective Field Theory, from perturbation theory to lattice simulations. It allows us to study the…

High Energy Physics - Phenomenology · Physics 2017-10-23 Leonardo Pedro

For nonlinear inverse problems that are prevalent in imaging science, symmetries in the forward model are common. When data-driven deep learning approaches are used to solve such problems, these intrinsic symmetries can cause substantial…

Signal Processing · Electrical Eng. & Systems 2024-03-26 Wenjie Zhang , Yuxiang Wan , Zhong Zhuang , Ju Sun

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the…

Neural and Evolutionary Computing · Computer Science 2021-03-16 Anil Yaman , Giovanni Iacca , Decebal Constantin Mocanu , Matt Coler , George Fletcher , Mykola Pechenizkiy

This review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine…

Robotics · Computer Science 2022-02-28 Birgitta Dresp-Langley

Spontaneous synchronization has long served as a paradigm for behavioral uniformity that can emerge from interactions in complex systems. When the interacting entities are identical and their coupling patterns are also identical, the…

Disordered Systems and Neural Networks · Physics 2016-12-30 Takashi Nishikawa , Adilson E. Motter

The concept of symmetry breaking and the emergence of corresponding local order parameters constitute the pillars of modern day many body physics. The theory of quantum entanglement is currently leading to a paradigm shift in understanding…

Quantum Physics · Physics 2016-11-18 V. Zauner , D. Draxler , L. Vanderstraeten , J. Haegeman , F. Verstraete

A geometric mechanism that may, in analogy to similar notions in physics, be considered as "symmetry breaking" in geometry is described, and several instances of this mechanism in differential geometry are discussed: it is shown how…

Differential Geometry · Mathematics 2022-06-28 Andreas Fuchs , Udo Hertrich-Jeromin , Mason Pember

Spontaneous symmetry breaking in nonlinear systems provides a unified method to understand vastly different phenomena, ranging from Higgs mechanism [1] and superconductivity [2] to ecological stability [3] and genome generation [4].…

Optics · Physics 2022-08-11 Chaohan Cui , Liang Zhang , Linran Fan

It has been demonstrated that one of the most striking features of the nervous system, the so called 'plasticity' (i.e high adaptability at different structural levels) is primarily based on Hebbian learning which is a collection of…

Adaptation and Self-Organizing Systems · Physics 2007-05-23 G. Szirtes , Zs. Palotai , A. Lorincz

Learning features invariant to arbitrary transformations in the data is a requirement for any recognition system, biological or artificial. It is now widely accepted that simple cells in the primary visual cortex respond to features while…

Neural and Evolutionary Computing · Computer Science 2020-12-14 Jayanta K. Dutta , Bonny Banerjee

We propose a fundamental setup for the realization of spontaneous symmetry breaking (SSB) and spontaneous antisymmetry breaking (SASB) in the framework of the nonlinear Schroedinger equation with the self-attractive and repulsive cubic…

Pattern Formation and Solitons · Physics 2025-07-15 Hidetsugu Sakaguchi , Boris A. Malomed , T. J. Taiwo

We present a clear and mathematically simple procedure explaining spontaneous symmetry breaking in quantum mechanical systems. The procedure is applicable to a wide range of models and can be easily used to explain the existence of a…

Classical Physics · Physics 2010-04-29 Jasper van Wezel , Jeroen van den Brink

In this work the spontaneous symmetry breaking in certain nonlinear theories with second-class constraints is explored. Using the Dirac's method we perform an analysis of the constraints and the counting of the degrees of freedom. The…

High Energy Physics - Theory · Physics 2022-09-14 C. A. Escobar , Román Linares

From the perfect radial symmetries of radiolarian mineral skeletons to the broken symmetry of homochirality, the logic of Nature's regularities has fascinated scientists for centuries. Some of Nature's symmetries are clearly visible in…

Spontaneous symmetry breaking (SSB) is a key concept in physics that for decades has played a crucial role in the description of many physical phenomena in a large number of different areas, like particle physics, cosmology, and…

Statistical Mechanics · Physics 2021-08-25 J. Smits , H. T. C. Stoof , P. van der Straten

The solution of problems in physics is often facilitated by a change of variables. In this work we present neural transformations to learn symmetries of Hamiltonian mechanical systems. Maintaining the Hamiltonian structure requires novel…

Computational Physics · Physics 2019-06-12 Roberto Bondesan , Austen Lamacraft