Principles of Protein Processing for a Self-Organising Associative Memory

Omer Qadir, Jerry Liu, Jon Timmis, Gianluca Tempesti, Andy Tyrrell

Research output: Contribution to journalArticlepeer-review


The evolution of Artificial Intelligence has passed through many phases over the years, going from rigorous mathematical grounding to more intuitive bio-inspired approaches. Despite the abundance of AI algorithms and machine learning techniques, the state of the art still fails to capture the rich analytical properties of biological beings or their robustness. Most parallel hardware architectures tend to combine Von Neumann style processors to make a multi-processor environment and computation is based on Arithmetic and Logic Units (ALU). This paper introduces an alternate architecture that is inspired from the biological world, and is fundamentally different from traditional processing which uses arithmetic operations. The architecture proposed here is targeted towards robust artificial intelligence applications.

Original languageEnglish
Pages (from-to)3836-3843
Number of pages8
Journal2010 ieee congress on evolutionary computation (cec)
Publication statusPublished - Jul 2010



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