Abstract
Hippocampus plays an important role in processing episodic memory. The different patterns of multi-unit activity (MUA) of CA1 neurons in hippocampus corresponds to the different high order functions of the brain such as memory, association, planning, action decision, etc. In this paper, a deep learning model, which is a composition of convolutional neural network (CNN) and support vector machine (SVM), is adopted to classify 4 kinds of episodic memories of a male rat: restraint stress (restraint), contact with a female rat (female), contact with a male rat (male), and contact with a novel object (object). In addition, the characteristic patterns of the different events occurred in CA1 neurons are specified by the feature explanation of CNN using Grad-CAM. As the result, this study suggests that it is available to recognize episodic memories by MUA signals and vice versa.
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