Linking brain behavior to underlying cellular mechanisms via large-scale brain modeling and simulation Academic Article uri icon


MeSH Major

  • Antineoplastic Agents
  • Carcinoma, Non-Small-Cell Lung
  • Lung Neoplasms


  • To understand brain behaviors, it is important to directly associate the network level activities to the underlying biophysical mechanisms, which require large-scale simulations with biophysically realistic neural models like Hodgkin-Huxley models. However, when simulations are conducted on models with sufficient biophysical details, great challenges arise from limited computer power, thereby restricting most existing computational works with biophysical models only to small-scale networks. On the other hand, with the emergence of powerful computing platforms, many recent works are geared to performing large-scale simulations with simple spiking models. However, the applicability of those works is limited by the nature of the underlying phenomenological model. To bridge the gap, an intermediate step is taken to construct a scalable brain model with sufficient biophysical details. In this work, great efforts are devoted to taking into account not only local cortical microcircuits but also the global brain architecture, and efficient techniques are proposed and adopted to address the associated computational challenges in simulation of networks of such complexity. With the customized simulator developed, we are able to simulate the brain model to generate not only sleep spindle and delta waves but also the spike-and-wave pattern of absence seizures, and directly link those behaviors to underlying biophysical mechanism. Those initial results are interesting because they show the possibility to determine underlying causes of diseases by simulating the biologically realistic brain model. With further development, the work is geared to assisting the clinicians in selecting the optimal treatment on an individual basis in the future. © 2012 Elsevier B.V.

publication date

  • November 15, 2012



  • Academic Article


Digital Object Identifier (DOI)

  • 10.1016/j.neucom.2012.05.020

Additional Document Info

start page

  • 317

end page

  • 331


  • 97