LOCAL LEARNING IN NETWORKS OF UNIVERSAL ANALOGIC NEURONS

Biological neural systems exhibit the property of locality in all the calculations and structures. Classical artificial neural networks normally use an external system that performs some of the operations, mainly the learning algorithm. This arrangement means a strong dependence on external programs and machines. Learning algorithms must be implemented with local computations. Each unit has to be able to estimate its own contribution to the global error, according to the information about the errors of other units and local information. If all the modules are similar in physical connection characteristics, we can have a universal type of parametric modules. The desired final development is a general model in which all known neural network models conform. Self-programming is accomplished by means of an internal algorithm in the module. The learning is the adjustment of model parameters (indeed structural parameters). In this paper, the emphasis is on a particular case to illustrate the possibilities of inserting learning into the modules forming the network.