• DocumentCode
    2766318
  • Title

    A Neural Network Model of the Connectivity of the Biological Somatic Sensors

  • Author

    Rosen, Alan ; Rosen, David

  • Author_Institution
    Machine Consciousness Inc., Redondo Beach
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    299
  • Lastpage
    306
  • Abstract
    The connectivity of a neural network model is designed to be similar to the biological connectivity of the somatic body sensors. The model consists of a mechanical robot controlled by a neural network based controller that adheres to three functional characteristics commonly associated with the subjective experience of sensory sensations (modalities of sensors): a) self knowledge, b) a "world space"-coordinate system in a controller, and c) access to information. The robotic controller, called a relational robotic controller (RRC)-circuit, controls the robotic body by reverse engineering the operation of the animal and human body and brain so that the functional operation adheres to those three functional characteristics. The RRC-circuit model may lead to a sensory-motor control system of the somatic motor system and insight into the biological pathways in the brain and the overall functional operation of the human body and brain.
  • Keywords
    biosensors; neural nets; neurocontrollers; robots; biological somatic sensors connectivity; mechanical robot; neural network; neural network model; relational robotic controller; reverse engineering; robotic controller; somatic motor system; Biological neural networks; Biological system modeling; Biosensors; Control system synthesis; Mechanical sensors; Neural networks; Orbital robotics; Robot control; Robot sensing systems; Sensor phenomena and characterization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2006. IJCNN '06. International Joint Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9490-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2006.246695
  • Filename
    1716106