Abstract
ABSTRACT
A self-organizing cognitive network is mapped here onto the Id network model. The weight-vectors in this network represent some important topographical and biophysical parameters in the antibody-antigen affinity landscape. The Kohonen layers in the network correspond to affinity clones and the involved algorithm simulates the operations of clonal selection, hypermutation, differentiation, diversity, and affinity maturation. Two significant features of this model are: (i) a computationally feasible and biophysically informative representation of the para/epitopes, and (ii) the ability to perform simultaneous (parallel) and associative computations in a multidimensional shape-space. Computational experiments with real data have shown cognitive properties of this network. The results also indicate scope in quantitative characterization of the metadynamics of the above operations/weights in the adaptive development of the antibody repertoire.
Key words:
antibody shape-space, cognitive networks, immune network model, weight-vectors for probabilistic learning.
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