Konstruktiokeraamien liittäminen

. We introduce the concept of generalization for models of functional neuroactivation, and show how it is affected by the number, N , of neuroimaging scans available. By plotting generalization as a function of N (i.e. a "learning curve") we demonstrate that while simple, linear models may generalize better for small N 's, more flexible, low-biased nonlinear models, based on artificial neural networks (ANN's), generalize better for larger N 's. We demonstrate that for sets of scans of two simple motor tasks---one set acquired with [O 15 ]water using PET, and the other using fMRI---practical N 's exist for which "generalization crossover" occurs. This observation supports the application of highly flexible, ANN models to sufficiently large functional activation datasets. Keywords: Multivariate brain modeling, ill-posed learning, generalization, learning curves. 1 Introduction Datasets that result from functional activation studies of the living, human brain typically consist of two ..