Implementing neural networks in silicon

Seth Wolpert, Evangelia Micheli-Tzanakou

Research output: Chapter in Book/Report/Conference proceedingChapter


In spite of dramatic increases in the capacity and throughput of automated systems, there remain a number of descriptively simple yet highly desirable tasks that have remained elusive. These tasks are associated with the process known as pattern recognition. If machines were able to identify patterns in electrical, visual, mechanical, acoustic, or chemical signals as quickly and reliably as living systems, our world would be a very different place. A number of tedious operations could be performed tirelessly and accurately. We would no longer have the need for locks on our automobiles and homes or keyboards on our computers. For many years, engineers and mathematicians have worked to perform computer-based pattern recognition using geometric and statistical methods, but levels of accuracy commensurate with those of human operators have been difficult to obtain. To address the overwhelming utility to perform these tasks, engineers have begun to take cues from biological systems, the simplest of which are able to perform pattern recognition with relative ease and high reliability, as a matter of their very survival.

Original languageEnglish (US)
Title of host publicationSupervised and Unsupervised Pattern Recognition
Subtitle of host publicationFeature Extraction and Computational Intelligence
PublisherCRC Press
Number of pages24
ISBN (Electronic)9781420049770
ISBN (Print)9780849322785
StatePublished - Jan 1 2017

All Science Journal Classification (ASJC) codes

  • General Engineering


Dive into the research topics of 'Implementing neural networks in silicon'. Together they form a unique fingerprint.

Cite this