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We study low-dimensional dynamics in a Kuramoto model with inertia and Hebbian learning. In this model, the coupling strength between oscillators depends on the phase differences between the oscillators and changes according to a Hebbian learning rule. We analyze the special case of two coupled oscillators, which yields a five-dimensional dynamical system that decouples into a two-dimensional longitudinal system and a three-dimensional transverse system. We readily write an exact solution of the longitudinal system, and we then focus our attention on the transverse system. We classify the stability of the transverse system's equilibrium points using linear stability analysis. We show that the transverse system is dissipative and that all of its trajectories are eventually confined to a bounded region. We compute Lyapunov exponents to infer the transverse system's possible limiting behaviors, and we demarcate the parameter regions of three qualitatively different behaviors. Using insights from our analysis of the low-dimensional dynamics, we examine the original high-dimensional system in a situation in which we draw the intrinsic frequencies of the oscillators from Gaussian distributions with different variances.
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http://dx.doi.org/10.1063/5.0092378 | DOI Listing |
J Phys Chem Lett
September 2025
Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics, Central South University, Changsha, Hunan 410083, China.
The optoelectronic properties of perovskite/two-dimensional (2D) material van der Waals heterojunctions provide greater potential for innovative neuromorphic devices. However, the traditional growth of heterojunctions still relies on strict lattice matching and high-temperature processes, which hinder high-quality interface construction and efficient carrier transport. Here, the 2D CsPbI/MoS heterojunction is realized via the van der Waals epitaxy process, overcoming lattice matching limitations.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Faculty of Science, Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.
Predictive coding (PC) proposes that our brains work as an inference machine, generating an internal model of the world and minimizing predictions errors (i.e., differences between external sensory evidence and internal prediction signals).
View Article and Find Full Text PDFUnlabelled: Fragile X syndrome (FXS), the most common monogenic neurodevelopmental disorder associated with autism and intellectual disability, results from the loss of expression of the gene. Synaptic and circuit-level abnormalities are well documented in FXS and extensively studied in the KO mouse model. In CA1 hippocampal neurons functional, molecular and structural synaptic changes have been described yet the canonical form of Hebbian CA1 long term potentiation (LTP) remains intact in KO mice.
View Article and Find Full Text PDFbioRxiv
August 2025
Department of Neurobiology, Duke University, Durham, NC, USA.
Reinforcement learning (RL) offers a compelling account of how agents learn complex behaviors by trial and error, yet RL is predicated on the existence of a reward function provided by the agent's environment. By contrast, many skills are learned without external guidance, posing a challenge to RL's ability to account for self-directed learning. For instance, juvenile male zebra finches first memorize and then train themselves to reproduce the song of an adult male tutor through extensive practice.
View Article and Find Full Text PDFSci Rep
August 2025
Institute of Technical Physics and Materials Science, HUN-REN Centre for Energy Research, P.O. Box 49, 1525, Budapest, Hungary.
The exploration of brain networks has reached an important milestone as relatively large and reliable information has been gathered for connectomes of different species. Analyses of connectome data sets reveal that the structural length follows the exponential rule, the distributions of in- and out-node strengths follow heavy-tailed lognormal statistics, while the functional network properties exhibit powerlaw tails, suggesting that the brain operates close to a critical point where computational capabilities and sensitivity to stimulus is optimal. Because these universal network features emerge from bottom-up (self-)organization, one can pose the question of whether they can be modeled via a common framework, particularly through the lens of criticality of statistical physical systems.
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