Compensation type algorithms for neural nets: Stability and convergence

DOI10.1007/BF00275816zbMath0715.92003WikidataQ52118654 ScholiaQ52118654MaRDI QIDQ752028

Ingolf E. Dammasch, Ludwig J. Cromme

Publication date: 1989

Published in: Journal of Mathematical Biology (Search for Journal in Brave)

zbMATH Keywords

stabilityconvergencefixed pointsconnectivitysimulationsbehaviormemoryequilibrium stateAMPcognitive systemsaverage membrane potentialcompensation type algorithmexistence of compensated networksMcCulloch-Pitts networksneural modelingPlasticity of synaptic connectionstemporal development of neural networks

Mathematics Subject Classification ID

Neural biology (92C20) Circuits, networks (94C99)

Related Items (3)

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Cites Work

• Pacemaker neuron model with plastic firing rate: Entrainment and learning ranges
• Self-stabilization of neuronal networks. I. The compensation algorithm for synaptogenesis
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• The computation of fixed points and applications
• ON THE PROPERTIES OF RANDOMLY CONNECTED McCULLOCH-PITTS NETWORKS: DIFFERENCES BETWEEN INPUT-CONSTANT AND INPUT-VARIANT NETWORKS
• Simplicial and Continuation Methods for Approximating Fixed Points and Solutions to Systems of Equations
• A logical calculus of the ideas immanent in nervous activity
• Unnamed Item

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