Optimal firing in sparsely-connected low-activity attractor networks

DOI10.1007/BF00209419zbMath0848.92004MaRDI QIDQ1916511

Isaac Meilijson, Eytan Ruppin

Publication date: 28 October 1996

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

zbMATH Keywords

cortical neuronsfiring ratesdistorted versionsHebbian-like attractor neural networksoptimal activation functionsparsely connected low-activity networksstored memory patternsthreshold-sigmoid

Mathematics Subject Classification ID

Neural biology (92C20)

Cites Work

• Examining the volume efficiency of the cortical architecture in a multi- processor network model
• Modeling Brain Function
• Optimal signalling in attractor neural networks
• History-dependent attractor neural networks
• What determines the capacity of autoassociative memories in the brain?
• Quantitative study of attractor neural network retrieving at low spike rates: I. substrate—spikes, rates and neuronal gain
• Neurons with graded response have collective computational properties like those of two-state neurons.

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