Keywords:Living neuronal network, Stacked-autoencoder, Multi-layered artificial neuronal network, Feature extraction, Brain science
To elucidate the brain function, it is important to understand dynamics of nonlinear neuronal activity patterns. In this study, we attempted to extract features of activity patterns including evoked responses in the cultured rat neuronal network. Thus, we employed the multi-layered artificial neural network (ml-ANN) as the Deep-Learning method with stacked-autoencoder as the pre-training method to classify the network activity. As the result, activity pattern 2 seconds after the electrical stimulation was barely classified, however, discrimination ability of ml-ANN against the activity pattern of later time domain after the stimulation was not enough to classify the patterns of evoked responses, because of the insufficient amount of learning-data, which is difficult to be gathered in large quantities. This indicates a huge number of pre-learning data is absolutely necessary to improve the discrimination accuracy in order to identify patterns by the Deep-Learning method for large phenomena with "fluctuation" such as neural activity.