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Improvement of signal transmission through spike-timing-dependent plasticity in neural networks

S. Wang, J. Xu, F. Liu and W. Wang

Eur. Phys. J. B, 39 3 (2004) 351-356

DOI: https://doi.org/10.1140/epjb/e2004-00200-4

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EPJ A Topical Collection devoted to the legacy of Peter Schuck
EPJ A Topical Collection: Radiative Corrections: From Medium to High Energy Experiments
EPJ A Topical Collection: Short-Range Correlations and the EMC Effect
EPJ A Topical Issue: Heavy and Super-Heavy Nuclei and Elements: Production and Properties
EPJ B Topical Issue: Extreme Value Statistics and Search in Biology: Theory and Simulations
EPJ D Topical Issue: Dynamics and Photodynamics: from isolated molecules to the condensed phase
EPJ D Topical Issue: Physics of Ionized Gases and Spectroscopy of Isolated Complex Systems: Fundamentals and Applications (2022)
EPJ D Topical Issue: Precision Physics of Simple Atomic Systems
EPJ D Topical Issue: Quantum Walks and applications
EPJ E Topical Issue: 50 years of Small Angle Neutron Scattering at the ILL in Grenoble
EPJ E Topical Issue: Festschrift in honor of Philip (Fyl) Pincus
EPJ E Topical Issue: Novel Molecular Materials and Devices from Functional Soft Matter
EPJ E Topical Issue: Quantitative AI in Complex Fluids and Complex Flows: Challenges and Benchmarks
EPJ H Special Issue: History for Physics: Contextualizing modern developments in the foundations of quantum theory
EPJ Plus Focus Point Issue: Advances in cryogenic detectors for dark matter, neutrino physics and astrophysics
EPJ Plus Focus Point Issue: Citizen science for physics: From Education and Outreach to Crowdsourcing fundamental research
EPJ Plus Focus Point Issue: Focus Point on Higher Derivatives in Quantum Gravity: Theory, Tests, Phenomenology
EPJ Plus Focus Point Issue: The Magic of Glass
EPJ Quantum Technology - Open Calls for Papers
EPJ ST Special Issue: Application of Fractional-Calculus in Physical Systems
EPJ ST Special Issue: Framework of Fractals in Data Analysis: Theory and Interpretation
EPJ ST Special Issue: Machine Learning for quantum many-body systems
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