## 5 Experiments and Comparisons of Deep Learning Architectures
### Exploratory Data Analysis  
The signal we utilize contains data from a single electrode, as opposed to previous studies that employed MIMO techniques [27]. To extract the maximum information from this signal, we conduct exploratory data analysis to study and summarize the main characteristics of the obtained fEPSP signal datasets, as shown in Figure 6. As observed, most features exhibit a smooth dependence of the mean value on the stimulus amplitude. However, outliers are present in all cases, indicated by the blue dots in the figure.  
Note that, for some parameters, such as the decrease time (Figure 6b) and slope (Figure 6e), the variance is quite limited, except in the case of 100 \(\upmu\)A amplitude stimuli. However, for other parameters, like response halfwidth, the variance is generally sufficient in almost all cases (Figure 6c).  
The variance for rise time (Figure 6a) and amplitude (Figure 6d) strongly depends on the amplitude of the stimulus. We can distinguish groups of signals with relatively small dispersion in these parameters and others with a much stronger dispersion. Additionally, it is noteworthy that the CA3 and CA1 signals can also be statistically distinguished.  
Using principal component analysis (PCA), we identified the most significant features of the signals. Based on the results of this analysis, weight coefficients were selected in a custom metric (see Section 4.3), which was subsequently used to assess the quality of the predicted signal.  
Analyzing the statistical properties of these parameters leads us to consider the possible existence of clusters in the data. Identifying such clusters could help us understand qualitative changes in the slice's response to the stimulus as the stimulation amplitude changes, which could have implications for deep learning algorithms.  
To test this hypothesis, we employ the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm, which allows us to identify clusters without prior knowledge of their quantity.  
As shown, the method identified three clusters (classes) in the data. The first class comprises the noisiest signals. In the second class, the response begins with a short-term and small amplitude increase in the fEPSP level, while the third class exhibits the most characteristic response to a rectangular pulse. Examples of all signal types are depicted in Figure 7.  
Figure 6: Boxplots for main features used in custom metric of CA3 signals (left panel) and CA1 signals (right panel) obtained as a response to short rectangular electrical pulse of varying amplitude. (**a**) Rise time, (**b**) decrease time, (**c**) halfwidth, (**d**) amplitude, (**e**) slope. Black dots are outliers.  
Considering the choice of evaluation metrics and the design of the learning process, we anticipate that our deep neural networks will provide less accurate predictions for signals belonging to Classes 1 and 2. Lower prediction quality for highly noisy signals (Class 1) is expected. The difficulty in predicting signals of a specific shape belonging to Class 2 arises from their unique response form and their relatively infrequent occurrence. Improving the accuracy of prediction for such signals requires further investigation.