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.
D. Remondini, P. Tieri, S. Valensin, E. Verondini, C. Franceschi, F. Bersani, et al. (2006). A general learning rule for network modeling of neuroimmune interactome. BERLIN : Springer.
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.File in questo prodotto:
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