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Evidence indicates that sharp-wave ripples (SWRs) are primary network events supporting memory processes. However, some studies demonstrate that even after disruption of awake SWRs the animal can still learn spatial task or that SWRs may be not necessary to establish a cognitive map of the environment. Moreover, we have found recently that despite a deficit of sleep SWRs the APP/PS1 mice, a model of Alzheimer’s disease, show undisturbed spatial reference memory. Searching for a learning-related alteration of SWRs that could account for the efficiency of memory in these mice we use convolutional neural networks (CNN) to discriminate pre- and post-learning 256 ms samples of LFP signals, containing individual SWRs. We found that the fraction of samples that were correctly recognized by CNN in majority of discrimination sessions was equal to ~ 50% in the wild-type (WT) and only 14% in APP/PS1 mice. Moreover, removing signals generated in a close vicinity of SWRs significantly diminished the number of such highly recognizable samples in the WT but not in APP/PS1 group. These results indicate that in WT animals a large subset of SWRs and signals generated in their proximity may contain learning-related information whereas such information seem to be limited in the AD mice.

Recognition of post-learning alteration of hippocampal ripples by convolutional neural network differs in the wild-type and AD mice