Some Insights Into Computational Models of (Patho)physiological Brain Activity

• Published in: Proceedings of the IEEE 2006, Vol.94 (4), p.784-804
• Main Authors: Suffczynski, P., Wendling, F., Bellanger, J.-J., Da Silva, F.H.L.
• Format: Article
• Language: English
• Subjects: Anatomy; Anatomy & physiology; Biological neural networks; Biological system modeling; Biology computing; Brain; Brain modeling; Computation; Computational modeling; computational neuroscience; Computer networks; Computer simulation; Dynamical systems; Dynamics; Mathematical models; Nervous system; Networks; Neurons; Organizations; Physiology; simulation; Studies
• ISSN: 0018-9219; 1558-2256
• DOI: 10.1109/JPROC.2006.871773

The amount of experimental data concerning physiology and anatomy of the nervous system is growing very fast, challenging our capacity to make comprehensive syntheses of the plethora of data available. Computer models of neuronal networks provide useful tools to construct such syntheses. They can be used to interpret experimental data, generate experimentally testable predictions, and formulate new hypotheses regarding the function of the neural systems. Models can also act as a bridge between different levels of neuronal organization. The ultimate aim of computational neuroscience is to provide a link between behavior and underlying neural mechanisms. Depending on the specific aim of the model, there are different levels of neuronal organization at which the model can be set. Models are constructed at the microscopic (molecular and cellular), macroscopic level (local populations or systems), or dynamical systems level. Apart from purely computational models, hybrid networks are being developed in which biological neurons are connected in vitro to computer simulated neurons. Also, neuromorphic systems are recently being created using silicon chips that mimic computational operations in the brain. This paper reviews various computational models of the brain and insights obtained through their simulations.