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.

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Brain processes information through a complex hierarchical associative memory organization that is distributed across a complex neural network. The GBNN associative memory model has recently been proposed as a new class of recurrent clustered neural network that presents higher efficiency than the classical models. In this article, we propose computational simplifications and architectural optimizations of the original GBNN. This work leads to significant complexity and area reduction without affecting neither memorizing nor retrieving performance. The obtained results open new perspectives in the design of neuromorphic hardware to support large-scale general-purpose neural algorithms.

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