An artificial neural network approach for glomerular activity pattern prediction using the graph kernel method and the gaussian mixture functions.

This paper proposes a neural network model for prediction of olfactory glomerular activity aimed at future application to the evaluation of odor qualities. The model's input is the structure of an odorant molecule expressed as a labeled graph, and it employs the graph kernel method to quantify structural similarities between odorants and the function of olfactory receptor neurons. An artificial neural network then converts odorant molecules into glomerular activity expressed in Gaussian mixture functions. The authors also propose a learning algorithm that allows adjustment of the parameters included in the model using a learning data set composed of pairs of odorants and measured glomerular activity patterns. We observed that the defined similarity between odorant structure has correlation of 0.3-0.9 with that of glomerular activity. Glomerular activity prediction simulation showed a certain level of prediction ability where the predicted glomerular activity patterns also correlate the measured ones with middle to high correlation in average for data sets containing 363 odorants.