Solving some of the open problems using the principles of natural computing exposed in the previous chapter led to the idea of developing a computing paradigm called cellular computing. The structure of such a computing system is defined by a grid (often two-dimensional) of locally interconnected cells. Each cell may be in a number of states (ranging from 2 to infinity) and the state of a cell depends by its own previous state and the previous states of its neighbors through a nonlinear functional, which may be defined in different ways. This functional is associated with a practical implementation of the cell and includes a set of tunable parameters grouped as a gene vector [7]. By tuning the gene parameters one can achieve programmability, i.e. different emergent behaviors within the same basic cellular architecture.
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© 2008 Springer-Verlag Berlin Heidelberg
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(2008). Cellular Nonlinear Networks: State of the Art and Applications. In: Systematic Design for Emergence in Cellular Nonlinear Networks. Studies in Computational Intelligence, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76801-2_2
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DOI: https://doi.org/10.1007/978-3-540-76801-2_2
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