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.

A general learning rule for network modeling of neuroimmune interactome

REMONDINI, DANIEL;TIERI, PAOLO;VALENSIN, SILVANA;VERONDINI, ETTORE;FRANCESCHI, CLAUDIO;BERSANI, FERDINANDO;CASTELLANI, GASTONE
2006

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.
Neural Nets
286
292
LECTURE NOTES IN COMPUTER SCIENCE
D. Remondini; P. Tieri; S. Valensin; E. Verondini; C. Franceschi; F. Bersani; G. Castellani
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/59064
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