A general learning rule for network modeling of neuroimmune interactome

Authors:
D. Remondini;P. Tieri;S. Valensin;E. Verondini;C. Franceschi;F. Bersani;G. C. Castellani
Affiliations:
“L.Galvani” Interdipartimental Center for Biophysics, Bioinformatics and Biocomplexity;“L.Galvani” Interdipartimental Center for Biophysics, Bioinformatics and Biocomplexity;“L.Galvani” Interdipartimental Center for Biophysics, Bioinformatics and Biocomplexity;“L.Galvani” Interdipartimental Center for Biophysics, Bioinformatics and Biocomplexity;“L.Galvani” Interdipartimental Center for Biophysics, Bioinformatics and Biocomplexity;“L.Galvani” Interdipartimental Center for Biophysics, Bioinformatics and Biocomplexity;“L.Galvani” Interdipartimental Center for Biophysics, Bioinformatics and Biocomplexity
Venue:
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Year:
2005

Citing 3
Cited 0

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Abstract

We propose a network model in which the communication between its elements (cells, neurons and lymphocytes) can be established in various ways. The system evolution is driven by a set of equations that encodes various degrees of competition between elements. Each element has an “internal plasticity threshold” that, by setting the number of inputs and outputs, determines different network global topologies.