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We note that in probability theory, a product of probabilities usually assumes that evidence is independent. Expression for L contains a product over n, but it does not assume independence among various signals '''X'''(n). There is a dependence among signals due to [[(concept-models)]]: each model '''M<sub>m</sub>'''('''S<sub>m</sub>''',n) predicts expected signal values in many neurons n.
During the learning process, [[concept-models]] are constantly modified. In this review we consider a case when functional forms of [[models (concept-models)|models]] , '''M<sub>m</sub>'''('''S<sub>m</sub>''',n), are all fixed and learning-adaptation involves only model parameters, '''S<sub>m</sub>'''. From time to time a system forms a new concept, while retaining an old one as well; alternatively, old concepts are sometimes merged or eliminated. This requires a modification of the similarity measure L;
==References==
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