• DocumentCode
    2089189
  • Title

    Computational neurobiology meets semiconductor engineering

  • Author

    Hammerstrom, Dan

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Oregon Graduate Inst., Beaverton, OR, USA
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    3
  • Lastpage
    12
  • Abstract
    Many believe that the most important result to come out of the last ten years of neural network research is the significant change in perspective in the neuroscience community towards theory of computational neurobiology and functional neuro-models. Arriving on a fast moving train from the other direction is semiconductor technology, one of the greatest technology success stories of all time transistors are now approaching deep submicron (less than 100 nanometers) in size, and we will soon be building silicon chips with over 1 billion transistors. The marriage of these two technologies is creating what Andy Grove (ex-CEO of Intel) refers to as a strategic inflection point. Although previous attempts at merging these technologies were premature, silicon and computational neurobiology are now merging to create an extremely powerful, and radically new form of computation
  • Keywords
    neural nets; technological forecasting; computational neurobiology; neural network; neuro-models; semiconductor technology; Biology computing; Biomedical signal processing; Character recognition; Computer networks; Digital signal processing; Hidden Markov models; Intelligent robots; Signal processing algorithms; Silicon; Target recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multiple-Valued Logic, 2000. (ISMVL 2000) Proceedings. 30th IEEE International Symposium on
  • Conference_Location
    Portland, OR
  • ISSN
    0195-623X
  • Print_ISBN
    0-7695-0692-5
  • Type

    conf

  • DOI
    10.1109/ISMVL.2000.848593
  • Filename
    848593