Self-stabilization of neuronal networks. I. The compensation algorithm for synaptogenesis

DOI10.1007/BF00318417zbMath0596.92012WikidataQ52655006 ScholiaQ52655006MaRDI QIDQ1079138

J. R. Wolff, Günter P. Wagner, Ingolf E. Dammasch

Publication date: 1986

Published in: Biological Cybernetics (Search for Journal in Brave)

zbMATH Keywords

ontogenesiscompensation theory of synaptogenesisHebbian rules of plasticityMorphogenesisneuronal model networkrandomly connected McCulloch-Pitts networks

Mathematics Subject Classification ID

Stabilization of systems by feedback (93D15) Other natural sciences (mathematical treatment) (92F05) Physiological, cellular and medical topics (92Cxx)

Related Items (2)

A theoretical network model to analyse neurogenesis and synaptogenesis in the dentate gyrus ⋮ Compensation type algorithms for neural nets: Stability and convergence

Cites Work

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• Stabilization of Hebbian neural nets by inhibitory learning
• Adaptive pattern classification and universal recoding. II: Feedback, expectation, olfaction, illusions
• ON THE PROPERTIES OF RANDOMLY CONNECTED McCULLOCH-PITTS NETWORKS: DIFFERENCES BETWEEN INPUT-CONSTANT AND INPUT-VARIANT NETWORKS
• A logical calculus of the ideas immanent in nervous activity

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