## Abstract

The holographic conceptual approach to cognitive processes in the human brain suggests that, in some parts of the brain, each part of the memory (a neuron or a group of neurons) contains some information regarding the entire data.In Dolev and Frenkel (2010, 2012) we demonstrated how to encode data in a holographic manner using the Walsh-Hadamard transform. The encoding is performed on randomized information, that is then represented by a set of Walsh-Hadamard coefficients. These coefficients turn out to have holographic properties. Namely, any portion of the set of coefficients defines a "blurry image" of the original data.In this work, we describe a built-in error correction technique-enlarging the width of the matrix used in the Walsh-Hadamard transform to produce a rectangular Hadamard matrix. By adding this redundancy, the data can bear more errors, resulting in a system that is not affected by missing coefficients up to a certain threshold. Above this threshold, the loss of data is reflected by getting a "blurry image" rather than a concentrated damage. We provide a heuristic analysis of the ability of the technique to correct errors, as well as an example of an image saved using the system. Finally, we give an example of a simple implementation of our approach using neural networks as a proof of concept.

Original language | English |
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Pages (from-to) | 87-94 |

Number of pages | 8 |

Journal | Neural Networks |

Volume | 77 |

DOIs | |

State | Published - 1 May 2016 |

## Keywords

- Holographic memory
- Neural computation
- Neural networks
- Walsh-Hadamard

## ASJC Scopus subject areas

- Cognitive Neuroscience
- Artificial Intelligence