## 5 Experiments and Comparisons of Deep Learning Architectures
### Choosing Optimal Deep Learning Architecture for fEPSP Signal Prediction  
In order to select the optimal architecture for fEPSP signal prediction in the CA1 region based on the CA3 signal, we have compared two deep learning architectures: LSTM and RC. Figure 8 shows the example of true and predicted CA1 signals for LSTM (Figure 8a) and reservoir (Figure 8b) for stimulus amplitude equal to 400 \(\upmu\)A. All true and predicted signals for all stimulus amplitudes are presented in Appendix A. Comparison of the values of evaluation metrics for predicted signals highlights the ability of the two networks to capture and mimic certain features and characteristics of signals. As one can see from Figure 9, in general, both deep learning architectures allow  
Figure 8: True (blue) and predicted (red and black) fEPSP signals at 400 \(\upmu\)A stimulus amplitude for (**a**) LSTM (red) and (**b**) RC (black) networks. Figure 7: Typical examples of signals belonging to different classes. (**a**) Signals belonging to Class 1, (**b**) signals belonging to Class 2, (**c**) signals belonging to Class 3. See detailed description in the text. quite accurate prediction to be obtained. According to the MAPE metric, both networks showed approximately the same result (Figure 9a), except for the case of the 1000 \(\upmu\)A stimulus. Nevertheless, taking into account our custom weighted metric shows that in this case LSTM is able to predict valuable features of the signal quite exactly. RC in this case shows worse but still acceptable results. The custom weighted metric can also distinguish amplitudes, for which each network shows better accuracy (Figure 9b). Let us analyze how RC and LSTM deep neural networks predict specific features of signals that we take into account in our custom metric separately. In order to carry this out, we plot the true and predicted values of rise time, decrease time, halfwidth, amplitude and slope of the CA1 fEPSP signals, see Figure 10. As one can see from Figure 10a,b, both deep learning architectures predict the rise time and decrease time of the fEPSP CA1 quite accurately. Moreover, RC shows better accuracy compared to the LSTM. Nevertheless, LSTM copes better with the prediction of the halfwidth of fEPSP CA1 signals in most cases, while RC fails to predict this parameter for high stimulus amplitudes (800-1000 \(\upmu\)A), see Figure 10c. Figure 10d shows the results for the true and predicted amplitude of the CA1 fEPSP signals. As one can see, for lower amplitudes of stimuli (100-300 \(\upmu\)A), both deep networks show good accuracy, while for higher amplitudes of stimuli the prediction error is growing and LSTM shows slightly better accuracy than RC. As for the slope parameter, both networks shows approximately the same result, see Figure 10e. Predicted signal quality metrics show how accurately the biological signal was predicted by the neural network. This will make it possible to use the predicted signal as accurately as possible to restore activity in the damaged area in the mouse hippocampal slice in real time. This predicted signal will be sent to the stimulating electrode in real time when the fEPSP is recorded after the destruction in the CA3 area. Maximizing the quality of the predicted signal will allow us to most effectively \"add\" activity to the hippocampal slice to produce an output signal in the CA1 area similar to that recorded in the \"healthy\" hippocampus. To summarize, RC is able to predict CA1 fEPSP signals more accurately than LSTM on average taking into account different signal features separately. Also, RC has several other valuable advantages. First of all, it allows studying the inner structure of the reservoir that should statistically reflect features of neurotransmission in the slice for optimal parameters of the reservoir, namely, for an optimal number of neurons in the reservoir and the probability of couplings between them.