We study multifrequency Hebbian plasticity by analyzing phenomenological models of weakly connected neural networks. We start with an analysis of a model for single-frequency networks previously shown to learn and memorize phase differences between component oscillators. We then study a model for gradient frequency neural networks (GrFNNs) which extends the single-frequency model by introducing frequency detuning and nonlinear coupling terms for multifrequency interactions. Our analysis focuses on models of two coupled oscillators and examines the dynamics of steady-state behaviors in multiple parameter regimes available to the models. We find that the model for two distinct frequencies shares essential dynamical properties with the single-frequency model and that Hebbian learning results in stronger connections for simple frequency ratios than for complex ratios. We then compare the analysis of the two-frequency model with numerical simulations of the GrFNN model and show that Hebbian plasticity in the latter is locally dominated by a nonlinear resonance captured by the two-frequency model.
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http://dx.doi.org/10.1007/s00422-020-00854-6 | DOI Listing |
Mol Cell Proteomics
December 2024
Department of Pharmacology and Toxicology, University of Texas Medical Branch.
Mater Horiz
January 2025
School of Chemical Sciences, National Institute of Science Education and Research (NISER), An OCC of HBNI, Bhubaneswar, 752050, Odisha, India.
Neuromorphic and fully analog in-memory computations are promising for handling vast amounts of data with minimal energy consumption. We have synthesized and studied a series of homo-bimetallic silver purine MOFs (1D and 2D) having direct metal-metal bonding. The N7-derivatized purine ligands are designed to form bi-metallic complexes under ambient conditions, extending to a 1D or 2D metal-organic framework.
View Article and Find Full Text PDFJ Zhejiang Univ Sci B
April 2024
Department of Neurology and International Institutes of Medicine, the Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu 322000, China.
Stress has been considered as a major risk factor for depressive disorders, triggering depression onset via inducing persistent dysfunctions in specialized brain regions and neural circuits. Among various regions across the brain, the lateral habenula (LHb) serves as a critical hub for processing aversive information during the dynamic process of stress accumulation, thus having been implicated in the pathogenesis of depression. LHb neurons integrate aversive valence conveyed by distinct upstream inputs, many of which selectively innervate the medial part (LHbM) or lateral part (LHbL) of LHb.
View Article and Find Full Text PDFNat Commun
December 2024
Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, China.
Recent advances have uncovered an exotic sliding ferroelectric mechanism, which endows to design atomically thin ferroelectrics from non-ferroelectric parent monolayers. Although notable progress has been witnessed in understanding the fundamental properties, functional devices based on sliding ferroelectrics remain elusive. Here, we demonstrate the rewritable, non-volatile memories at room-temperature with a two-dimensional (2D) sliding ferroelectric semiconductor of rhombohedral-stacked bilayer MoS.
View Article and Find Full Text PDFPhys Rev E
November 2024
Physics Department, University of Trento, via Sommarive, 14 I-38123 Trento, Italy.
Network systems can exhibit memory effects in which the interactions between different pairs of nodes adapt in time, leading to the emergence of preferred connections, patterns, and subnetworks. To a first approximation, this memory can be modeled through a "plastic" Hebbian or homophily mechanism, in which edges get reinforced proportionally to the amount of information flowing through them. However, recent studies on glia-neuron networks have highlighted how memory can evolve due to more complex dynamics, including multilevel network structures and "metaplastic" effects that modulate reinforcement.
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