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This paper discusses the implementation of holographic associative memories through artificial neural networks (using mirror neurons). Such memories use a data structure based on random patterns to store and retrieve the desired information, presenting some features that are very similar to biological memory systems. This approach for data storage discards any training process as well as does not need complex procedures to retrieve the output data. As the information is distributed in the whole memory, some part of it may be corrupted by noise or even be deleted without any loss of essential information. An application example is shown and discussed.