Recognition of Brain Wave Related to the Episode Memory by Deep Learning Methods

Abstract

Hippocampus makes an important role of memory in the brain. In this chapter, a study of brain wave recognition using deep learning methods is introduced. The purpose of the study is to match the ripple-firings of the hippocampal activity to the episodic memories. In fact, brain spike signals of rats (300–10 kHz) were recorded and machine learning methods such as Convolutional Neural Networks (CNN), Support Vector Machine (SVM), a deep learning model VGG16, and combination models composed by CNN with SVM and VGG16 with SVM were adopted to be classifiers of the brain wave signals. Four kinds of episodic memories, that is, a male rat contacted with a female/male rat, contacted with a novel object, and an experience of restrain stress, were detected corresponding to the ripple waves of Multiple-Unit Activities (MUAs) of hippocampal CA1 neurons in male rats in the experiments. The experiment results showed the possibility of matching of ripple-like firing patterns of hippocampus to episodic memory activities of rats, and it suggests disorders of memory function may be found by the analysis of brain waves.