We show that a ring of unidirectionally delay-coupled spiking neurons may possess a multitude of stable spiking patterns and provide a constructive algorithm for generating a desired spiking pattern. More specifically, for a given time-periodic pattern, in which each neuron fires once within the pattern period at a predefined time moment, we provide the coupling delays and/or coupling strengths leading to this particular pattern. The considered homogeneous networks demonstrate a great multistability of various travelling time- and space-periodic waves which can propagate either along the direction of coupling or in opposite direction. Such a multistability significantly enhances the variability of possible spatio-temporal patterns and potentially increases the coding capability of oscillatory neuronal loops. We illustrate our results using FitzHugh-Nagumo neurons interacting via excitatory chemical synapses as well as limit-cycle oscillators.
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http://dx.doi.org/10.1063/1.3665200 | DOI Listing |
Sci Rep
January 2025
Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy.
Subtle gait and cognitive dysfunction are common in Parkinson's disease (PD), even before most evident clinical manifestations. Such alterations can be assumed as hypothetical phenotypical and prognostic/progression markers. To compare spatiotemporal gait parameters in PD patients with three cognitive status: cognitively intact (PD-noCI), with subjective cognitive impairment (PD-SCI) and with mild cognitive impairment (PD-MCI) in order to detect subclinical gait differences.
View Article and Find Full Text PDFSci Total Environ
January 2025
Civil Engineering Department, Indian Institute of Technology, Palakkad, Kerala, India.
This study investigates the spatio-temporal consistency of different MMDI formulations and their role in meteorological drought characterization uncertainty under historic and future climates using ERA5 reanalysis, and outputs from eight Coupled Model Intercomparison Project Phase 6 models, respectively, across different climate zones and shared socioeconomic pathways (SSP) in the Indian subcontinent. Six MMDI formulations namely the Standardized Precipitation Evaporation Index (SPEI), Reconnaissance Drought Index (RDI), and self-calibrated Palmer Drought Severity Index (scPDSI), Standardized Palmer Drought Index (SPDI), Standardized Moisture Anomaly Index (SZI) and Supply Demand Drought Index (SDDI) are used. A suite of analysis including agreement mapping, category difference analysis and uncertainty contribution analysis using global sensitivity analysis (GSA) are employed to quantify the consistency of MMDIs and uncertainty in drought characterization due to the MMDI formulation.
View Article and Find Full Text PDFSci Data
January 2025
Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Leipzig, 04103, Germany.
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, 519087, China; State Key Laboratory of Wetland Conservation and Restoration, School of Environment, Beijing Normal University, Beijing, 100875, China; Key Laboratory of Coastal Water Environmental Management and Water Ecological Restoration of Guang-dong Higher Education Institutes, Beijing Normal University, Zhuhai, 519087, China.
Neural Netw
January 2025
Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China; Intelligent Game and Decision Laboratory, China.
The Physics-informed Neural Network (PINN) has been a popular method for solving partial differential equations (PDEs) due to its flexibility. However, PINN still faces challenges in characterizing spatio-temporal correlations when solving parametric PDEs due to network limitations. To address this issue, we propose a Physics-Informed Neural Implicit Flow (PINIF) framework, which enables a meshless low-rank representation of the parametric spatio-temporal field based on the expressiveness of the Neural Implicit Flow (NIF), enabling a meshless low-rank representation.
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