• DocumentCode
    245492
  • Title

    Spiking brain models: Computation, memory and communication constraints for custom hardware implementation

  • Author

    Lansner, Anders ; Hemani, Ahmed ; Farahini, Nasim

  • Author_Institution
    Dept. of Comput. Biol., Stockholm Univ., Stockholm, Sweden
  • fYear
    2014
  • fDate
    20-23 Jan. 2014
  • Firstpage
    556
  • Lastpage
    562
  • Abstract
    We estimate the computational capacity required to simulate in real time the neural information processing in the human brain. We show that the computational demands of a detailed implementation are beyond reach of current technology, but that some biologically plausible reductions of problem complexity can give performance gains between two and six orders of magnitude, which put implementations within reach of tomorrow´s technology.
  • Keywords
    belief networks; bioelectric phenomena; biology computing; brain models; computational complexity; neural nets; neurophysiology; BCPNN model; Bayesian Confidence Propagation Neural Network; custom hardware implementation; human neural information processing; problem complexity; spiking brain model communication constraints; spiking brain model computation constraints; spiking brain model memory constraints; Brain models; Computational modeling; Mathematical model; Memory management; Neurons; Real-time systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Design Automation Conference (ASP-DAC), 2014 19th Asia and South Pacific
  • Conference_Location
    Singapore
  • Type

    conf

  • DOI
    10.1109/ASPDAC.2014.6742950
  • Filename
    6742950