1. A method for implementing object recognition in N-dimensional space by means of a network having multiple layers of recognition elements (cells), using a. a network system comprising a multitude of recognition elements grouped into layers wherein i. the layers are provided in a structure which is hierarchical in that the layers are ordered and layers that are higher in the hierarchical order have cells connected by links representing ownership to cells contained by layers that are lower in the hierarchical order, ii. the layers are assigned a multiplicity of certain key features which the cells in the respective layers are capable of recognizing and representing, with each layer being assigned a different unique group of features to recognize, with the features assigned to the higher layers being more complex assemblages of the simple features assigned to the lower layers in the hierarchy, iii. the layers are dynamic in size and in structure in that member cells may be created or destroyed and links between cells of adjoining layers may be created or destroyed so as to adapt to the data to be recognized, iv. the layer cells are equipped with a polarization vectors which indicate the type of object which each cell has recognized if the cell is capable of representing more than one type of object and which serves to determine the compatibility of said cells with cells of neighboring layers v. the layers are equipped with a rule for determining whether cells of subordinate layers should be included in receptive fields of cells of higher layers, b. the network being adaptive to the data in that it is provided with the capability to represent content data by the creation of cells at the locations where input data occur and tolerate the absence of cells where there are no data, said adaptivity being effected by a synthesis method which can be applied to successive pairs of network layers, said synthesis method, for any two given adjacent layers where the lower hierarchical layer contains cells representing recognition data and the higher hierarchical layer contains no cells or insufficient cells to account for all of the lower layer cells, the synthesis method comprising the steps of: i. finding a cell on the lower hierarchical layer that is unowned, ii. generating a new cell on the higher hierarchical layer and assigning to it the ownership of the lower previously unowned cell, iii. finding all further unowned cells on the lower layer in the neighborhood of the previously unowned cell and assigning them also to the new owning cell, iv. reevaluating or rerecognizing the new owning cell based on the new group of owned cells, v. repeating steps i through iv until there are no unowned cells on the lower hierarchical layer c. the network is provided with a training algorithm in which information flows through the network in both top-down and bottom-up directions to adapt each layer to the recognition results of the hierarchically adjacent layers and induce convergence to a persistent robust solution, said training algorithm comprising a method for adapting, training and converging the network through an iterative process in which cell ownership is modified and cells are created or destroyed to converge the state of the cells to a final persistent stable state of mutual reinforcement which represents a solution to the recognition problem, said solution being embodied in, and defined by, the cells present in the highest layer of the network, said method being implemented by performing the steps of i. Data Input: copying the input data to be recognized into the lowest hierarchical layer of the network, ii. Initial Construction: successively constructing preliminary elements on each next higher layer in the hierarchy from the previously-constructed next lower layer of the network by means of analytical formulae and/or pattern recognition algorithms to account for all occurrences of patterns on the lower layer, until the uppermost level in the network hierarchy has been reached and populated with cells representing tentative solutions, iii. Analysis: successively cascading recognition information from the cells of each higher layer in the hierarchy to the owned cells in lower layers in the hierarchy, said information serving to sharpen and improve the response of the accurate lower layer cells and to destroy or weaken the poorly defined lower layer cells, iv. Synthesis: constructing new and modifying existing elements on each next higher layer in the hierarchy as was done in the initial construction to again create sufficient elements on each layer to own all elements on the next lower layer, and v. Iterative Convergence: repeating the Analysis and Synthesis steps alternatingly until the network has converged and no longer changes.