Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning

DOI10.1162/089976601317098501zbMath0996.92001OpenAlexW2156259017WikidataQ46124130 ScholiaQ46124130MaRDI QIDQ2770864

Richard Kempter, Wulfram Gerstner, J. Leo van Hemmen

Publication date: 8 July 2002

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://infoscience.epfl.ch/record/97800/files/Kempter01.pdf

zbMATH Keywords

plasticitypiecewise linear neuron modelspike-based learning

Mathematics Subject Classification ID

Neural biology (92C20) Neural networks for/in biological studies, artificial life and related topics (92B20)

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Cites Work

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• Hebbian learning reconsidered: Representation of static and dynamic objects in associative neural nets
• A simplified neuron model as a principal component analyzer
• Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns
• Emergence of spatiotemporal receptive fields and its application to motion detection
• Computational consequences of temporally asymmetric learning rules. I: Differential Hebbian learning
• An Algorithm for Modifying Neurotransmitter Release Probability Based on Pre- and Postsynaptic Spike Timing

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