The Multiple-Unit Firing Activity of Hippocampal CA1 Neurons Reflects Recent Prior Experience

Abstract

The hippocampus plays an important role in the formation of episodic memory. To identify patterns of hippocampal firing activity specific to episodic memory, we performed Multiple-Unit Firing Activity (MUA) recognition using deep learning methods. Briefly, adult male rats habituated to their home cage experienced one of four experimental episodic stimuli (restraint stress, contact with a female rat, contact with a male rat, or contact with a novel object) for 10 minutes. The patterns of recorded brain spike signals (300–10 kHz) in hippocampal CA1 were classified using machine learning methods such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), deep learning model VGG16, and combination models composed of VGG16 with SVM or VGG19 with SVM. As a result, the model of VGG19 with SVM detected MUA with ripple-like wave firings corresponding to specific episodes, achieving a validation accuracy of 96.79% which was the highest recognition rate in all of deep learning models. The results suggest that MUA of CA1 containing ripple firings corresponds to specific episodic memories. By capturing ripple firings, MUA analysis can assess and diagnose memory function, which may help detect various cognitive disorders.