Clone-Based Encoded Neural Networks to Design Efficient Associative Memories

Abstract

In this paper, we introduce a neural network model named Clone based Neural Network (CbNN) to design associative memories. Neurons in CbNN can be cloned statically or dynamically which allows to increase the number of data that can be stored and retrieved. Thanks to their plasticity, CbNN can handle correlated information more robustly than existing models and thus provides better memory capacity. We experiment this model in Encoded Neural Networks also known as Gripon-Berrou neural networks. Numerical simulations demonstrate that memory and recall abilities of CbNN outperform state of art for the same memory footprint.

Exponential Stability of Hopfield Neural Network Model with Non-Instantaneous Impulsive Effects

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