The design of a neural network with a biologically motivated architecture.

An associative neural network whose architecture is greatly influenced by biological data is described. The proposed neural network is significantly different in architecture and connectivity from previous models. Its emphasis is on high parallelism and modularity. The network connectivity is enriched by recurrent connections within the modules. Each module is, effectively, a Hopfield net. Connections within a module are plastic and are modified by associative learning. Connections between modules are fixed and thus not subject to learning. Although the network is tested with character recognition, it cannot be directly used as such for real-world applications. It must be incorporated as a module in a more complex structure. The architectural principles of the proposed network model can be used in the design of other modules of a whole system. Its architecture is such that it constitutes a good mathematical prototype to analyze the properties of modularity, recurrent connections, and feedback. The model does not make any contribution to the subject of learning in neural networks.

On the behavior of some associative neural networks.

Since Hopfield published his work on an associative memory model, a large number of works have studied the model from several angles and showed in particular its weaknesses, and presented ways to overcome them. Most of the proposed solutions seem to us however not biologically plausible. In this paper we present a simple statistical analysis of two networks similar to the Hopfield net, and show that the usage of positive feedback enhances the net recognizing capability without jeopardizing the stability. We also describe a layered parallel network composed of modules, each module being a modified Hopfield net. We finally present computer simulation results to support our analytical findings. The most important principles of this network are supported by data from the world of neurobiology.