Implementation of neural networks for the identification of single molecules

The effectiveness of neural networks and the optimization of parameters for implementing neural networks were evaluated for use in the identification of single molecules according to their fluorescence lifetime. The best network architecture and training parameters were determined for both ideal and nonideal single-molecule fluorescence data. The effectiveness of the neural network is compared to that of the maximum likelihood estimator on the basis of its ability to correctly identify single molecules. For ideal single-molecule data, it was found that the neural networks and the maximum likelihood estimator perform approximately equally well. For nonideal single-molecule fluorescence data, neural networks were able to correctly identify a larger percentage of single-molecule events than the MLE method.