Cellular neural network

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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.

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.