Modular neural networks with Hebbian learning rule

The paper consists of two parts, each of them describing a learning neural network with the same modular architecture and with a similar set of functioning algorithms. Both networks are artificially partitioned into several equal modules according to the number of classes that the network has to recognize. Hebbian learning rule is used for network training. In the first network, learning process is concentrated inside the modules so that a system of intersecting neural assemblies is formed in each module. Unlike that, in the second network, learning connections link only neurons of different modules. Computer simulation of the networks is performed. Testing of the networks is executed on the MNIST database. Both networks directly use brightness values of image pixels as features. The second network has a better performance than the first one and demonstrates the recognition rate of 98.15%.