Synaptic Value Bounds for Optimizing Retrieval in Recurrent Neural Networks

We present an analysis of the range of values of synaptical connections to enhance the storage proprieties of neural networks. Consider a random connected system to which random patterns are shown; these patterns impose specific activities over neuron pairs that might be connected evoking long term synapse modifications. Two approaches are given. The first one focuses on the noise context and the second one focuses on the learning rule. We find that increasing the variability among the synapse values within a given range, both the quality and the speed retrieval increase