Analog neuromorphic computation: an application to compression

Fernando J. Pineda, Andreas G. Andreou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Nature has evolved computing engines whose intelligence and natural abilities are unrivaled by modern computers. To match Mother Nature's abilities, we must overcome the same difficulties faced by natural systems, and we must learn to perform reliable computing with unreliable components. Steps in this direction are being taken by several groups at the Applied Physics Laboratory and at The Johns Hopkins University Department of Electrical and Computer Engineering. The purpose of the work is twofold: (1) to explore algorithms based on physical and neural models of computation and (2) to develop useful applications. We describe the basic approach and an experimental electronic neural network for the decompression of one-dimensional signals.

Original languageEnglish (US)
Pages (from-to)82-85
Number of pages4
JournalJohns Hopkins APL Technical Digest (Applied Physics Laboratory)
Issue number1
StatePublished - Jan 1 1994
Externally publishedYes

ASJC Scopus subject areas

  • General Engineering
  • General Physics and Astronomy


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