Cellular neural network

Cellular neural networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighbouring units only.

According to the Chua and Yang definition:


 * A CNN is an N-dimensional regular array of elements (cells);
 * The cell grid can be for example a planar array with rectangular, triangular or hexagonal geometry, a 2-D or 3-D torus, a 3-D finite array, or a 3-D sequence of 2-D arrays (layers);
 * Cells are multiple input-single output processors, all described by one or just some few parametric functionals;
 * A cell is characterized by an internal state variable, sometimes not directly observable from outside the cell itself;
 * More than one connection network can be present, with different neighborhood sizes;
 * A CNN dynamical system can operate both in continuous (CT-CNN) or discrete time (DT-CNN);
 * CNN data and parameters are typically continuous values;
 * CNN operate typically with more than one iteration, i.e. they are recurrent networks.
 * Characterized by large sets of isles of eden and basins of attraction. Oftentimes coupled with Chua's circuit and Bart's amplifier. Chaos is a fundamental recreation possible via the CNN.

Typical applications include image processing, analyzing 3D surfaces, solving partial differential equations, reducing non-visual problems to geometric maps, modelling biological vision and other sensory-motor organs.

= References = L. Chua and L. Yang, "Cellular neural networks: theory ; applications "

refer to