Abhishek Tiwari:NEUROINFORMATICS: Difference between revisions
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'''''PLoS Computational Biology''''' Volume 2 | Issue 8 | AUGUST 2006 | |||
*'''The Ion Channel Inverse Problem: Neuroinformatics Meets Biophysics''' [[Image:5star.png]] | |||
'''Synopsis''' | |||
Ion channels are the building blocks of the information processing capability of neurons: any realistic computational model of a neuron must include reliable and effective ion channel components. With the growing availability of computational resources, numerical inverse approaches are increasingly used across a range of disciplines. In this review paper,Cannon et al. suggest same type of inverse methodology for the study of ion channels. Inverse Problem approach address the question “what system gave rise to these observations?” usually by starting with a parameterized model that is expected to correspond to the real system for some point in its parameter space. A computational model of the recording process is built so that it can take any set of parameters and generate the data that they would have given rise to in exactly the same format as the experimental data. The model can then be compared to the real system in the space—that of the real data—where the most information is present. The forward calculation is then repeated over and over for different parameter sets guided by an optimization process to find the model or models that best represent the data. | |||
Perhaps the ion channel inverse problem can be the first instance of this philosophy spreading across the boundary into neuroinformatics. | |||
Revision as of 00:20, 9 September 2006
NEUROINFORMATICS
PLoS Computational Biology Volume 2 | Issue 8 | AUGUST 2006
- The Ion Channel Inverse Problem: Neuroinformatics Meets Biophysics
Synopsis
Ion channels are the building blocks of the information processing capability of neurons: any realistic computational model of a neuron must include reliable and effective ion channel components. With the growing availability of computational resources, numerical inverse approaches are increasingly used across a range of disciplines. In this review paper,Cannon et al. suggest same type of inverse methodology for the study of ion channels. Inverse Problem approach address the question “what system gave rise to these observations?” usually by starting with a parameterized model that is expected to correspond to the real system for some point in its parameter space. A computational model of the recording process is built so that it can take any set of parameters and generate the data that they would have given rise to in exactly the same format as the experimental data. The model can then be compared to the real system in the space—that of the real data—where the most information is present. The forward calculation is then repeated over and over for different parameter sets guided by an optimization process to find the model or models that best represent the data.
Perhaps the ion channel inverse problem can be the first instance of this philosophy spreading across the boundary into neuroinformatics.
