Autoassociative memory

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Autoassociative memory, also known as auto-association memory or an autoassociation network, is any type of memory that enables one to retrieve a piece of data from only a tiny sample of itself. It is often misunderstood to be only a form of backpropagation or other neural networks[citation needed].

Background[edit]

Traditional memory[edit]

Traditional memory[clarification needed] stores data at a unique address and can recall the data upon presentation of the complete unique address.

Autoassociative memory[edit]

Autoassociative memories are capable of retrieving a piece of data upon presentation of only partial information[clarification needed] from that piece of data.

Examples[edit]

For example, the sentence fragments presented below are sufficient for most humans to recall the missing information.

  1. "To be or not to be, that is _____."
  2. "I came, I saw, _____."

Most readers will realize the missing information is in fact:

  1. "To be or not to be, that is the question."
  2. "I came, I saw, I conquered."

This demonstrates the capability of autoassociative networks to recall the whole by using some of its parts.

Heteroassociative memory[edit]

Heteroassociative memories, on the other hand, can recall an associated piece of datum from one category upon presentation of data from another category. Hopfield networks [1] have been shown [2] to act as autoassociative memory since they are capable of remembering data by observing a portion of that data.

Bidirectional associative memory[edit]

Bidirectional associative memories (BAM)[3] are artificial neural networks that have long been used for performing heteroassociative recall.

References[edit]

External links[edit]