Biology – Quantitative Biology – Neurons and Cognition
Scientific paper
2005-08-29
p. 527 in "Bayesian Inference and Maximum Entropy Methods in Science and Engineering" ed. by K. H. Knuth, A. E. Abbda, R. D. M
Biology
Quantitative Biology
Neurons and Cognition
8 pages, 8 figures. Presented at 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and
Scientific paper
10.1063/1.2149834
Magnetoencephalographic (MEG) measurements record magnetic fields generated from neurons while information is being processed in the brain. The inverse problem of identifying sources of biomagnetic fields and deducing their intensities from MEG measurements is ill-posed when the number of field detectors is far less than the number of sources. This problem is less severe if there is already a reasonable prior knowledge in the form of a distribution in the intensity of source activation. In this case the problem of identifying and deducing source intensities may be transformed to one of using the MEG data to update a prior distribution to a posterior distribution. Here we report on some work done using the maximum entropy method (ME) as an updating tool. Specifically, we propose an implementation of the ME method in cases when the prior contain almost no knowledge of source activation. Two examples are studied, in which part of motor cortex is activated with uniform and varying intensities, respectively.
Lee HC
Pai Hung-I
Tseng Chih-Yuan
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