Inversion of positron annihilation lifetime spectroscopy, based on a neural
network Hopfield model, is presented in this paper. From a previous report
ed density function for lysozyme in water a simulated spectrum; without the
superposition of statistical fluctuation and spectrometer resolution effec
ts, was generated. These results were taken as the exact results from which
the neural network was trained. The precision of the inverted density func
tion was analyzed taking into account the number of neurons and the learnin
g time of the neural network. A fair agreement was obtained when comparing
the neural network results with the exact results. For example, the maximum
of the density function, with a precision of 0.4% for the percentual relat
ive error, was obtained for 64 neurons.