• DocumentCode
    1099690
  • Title

    The role of temporal parameters in a thalamocortical model of analogy

  • Author

    Choe, Yoonsuck

  • Author_Institution
    Dept. of Comput. Sci., Texas A&M Univ., College Station, TX, USA
  • Volume
    15
  • Issue
    5
  • fYear
    2004
  • Firstpage
    1071
  • Lastpage
    1082
  • Abstract
    How multiple specialized cortical areas in the brain interact with each other to give rise to an integrated behavior is a largely unanswered question. This paper proposes that such an integration can be understood under the framework of analogy and that part of the thalamus and the thalamic reticular nucleus (TRN) may be playing a key role in this respect. The proposed thalamocortical model of analogy heavily depends on a diverse set of temporal parameters including axonal delay and membrane time constant, each of which is critical for the proper functioning of the model. The model requires a specific set of conditions derived from the need of the model to process analogies. Computational results with a network of integrate and fire (IF) neurons suggest that these conditions are indeed necessary, and furthermore, data found in the experimental literature also support these conditions. The model suggests that there is a very good reason for each temporal parameter in the thalamocortical network having a particular value, and that to understand the integrated behavior of the brain, we need to study these parameters simultaneously, not separately.
  • Keywords
    brain models; IF neurons; axonal delay; integrate and fire neurons; membrane time constant; temporal parameters; thalamic reticular nucleus; thalamocortical model; Biomembranes; Brain modeling; Computer networks; Delay effects; Fires; Neurofeedback; Neurons; Neuroscience; Physiology; Relays; Action Potentials; Animals; Axons; Cerebral Cortex; Humans; Intralaminar Thalamic Nuclei; Models, Neurological; Nerve Net; Neural Pathways; Neurons; Synapses; Synaptic Transmission; Thalamus; Time Factors;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2004.832728
  • Filename
    1333072