## 2 Description of the Implemented Approach  
We propose an intelligent approach to restoring electrophysiological activity in mouse hippocampus slices using deep neural networks. The main idea of our approach can be described as follows. We stimulate the Dentate gyrus (DG) in healthy slices and obtain a subsequent \"healthy\" response in the form of field excitatory postsynaptic potentials (fEPSPs), first in the CA3 region and then in the CA1 region. In the case of violation of the conductivity of the neural signals along the perforant pathway, e.g., due to a mechanical trauma in the CA3 region, no signal is obtained from the CA3 region and a subtle \"unhealthy\" signal is received from the CA1 region in response to the DG stimulation according to the chosen protocol. In order to restore healthy activity in the CA1 region, we use the CA3 fEPSP as an input for our neural network to predict the CA1 fEPSP. This approach is schematically illustrated in Figure 1. Our method allows using the predicted signal to simulate healthy activity in damaged brain regions in real-time biological experiments [40]. The pipeline of our study is presented in Figure 2. First, we carried out a series of biological experiments to record fEPSPs in hippocampal slices in two areas (CA1 and CA3) under normal conditions during electrical stimulation of DG. As a result, we obtained a set of paired fEPSPs at different stimulus amplitudes. The detailed fEPSP registration protocol is described in Section 3. Subsequent procedures with the resulting set of signals involve standard preprocessing for biological signals, which consists of the following sequential steps: artifact removal (in the vicinity of 20 points in a specific time period, these points are replaced by those that were encountered previously with a shift of 150 points), 1000 Hz low pass filter processing, standardization, 11-point smoothing (linear approximation) and applying a Gaussian filter with standard deviation for the Gaussian kernel for all axes equal to 35, as well as averaging over all signals recorded for each stimulus amplitude. The sampling rate was 20 kHz. The resulting data containing pairs of averaged CA3-CA1 signals were divided into training and testing samples for further training and prediction of the output signal in the hippocampus using two deep learning architectures. A detailed description of the data collection protocol, as well as the data preprocessing routine, can be found in Section 3. A detailed description of the applied methodology, including the chosen deep learning architectures, as well as evaluation metrics and peculiarities of the training process, is presented in Section 4. Figure 1: (**a**) Scheme of the experiment on recording field excitatory postsynaptic potentials (fEPSPs) in mice hippocampal slices and scheme for training neural networks (LSTM or reservoir). The left panel shows the mouse hippocampal slices with a protocol for installing, recording and stimulating electrodes for electrical stimulation and subsequent synaptic transmission activation in the perforant (3-synaptic) pathway of the hippocampus. A stimulating electrode was placed in the DG area and sent electrical square current pulses to activate the cells in DG area of hippocampus. Recording electrodes were installed in the pyramidal neuron dendrites of CA3 and CA1, the hippocampus areas. This made it possible to record the activation of the perforant pathway in the hippocampus due to electrical stimulation. The following shows the original trace of the fEPSP recorded in the dendrites of CA3 and CA1 hippocampus areas. These signals were fed to the input of LSTM or reservoir for training. The right panel shows the architectures of the neural networks used. After training these neural networks with fEPSP signals recorded from the CA3 region in hippocampal slices (input signals), predicted signals for the CA1 region (output signals) were obtained. (**b**) Representative examples of original fEPSP traces at 400 \(\upmu\)A and 500 \(\upmu\)A stimulus amplitudes.