## 5 A 4-Bit SNN Working as an Episodic Memory
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**(a)**} & \multicolumn{3}{c}{**Single Junction Ratio: 0.7\(-\)0.3**} & \multicolumn{3}{c}{**(b)**} & \multicolumn{3}{c}{**Single Junction Ratio: 0.8\(-\)0.2**} & \multicolumn{3}{c}{**(c)**} & \multicolumn{3}{c}{**Single Junction Ratio: 0.9\(-\)0.1**} \\ \hline
**channel** & **1** & **2** & **3** & **4** & **channel** & **1** & **2** & **3** & **4** & **channel** & **1** & **2** & **3** & **4** \\ \hline
**INPUT** & 1 & 0 & 0 & 0 & **INPUT** & 1 & 0 & 0 & 0 & **INPUT** & 1 & 0 & 0 & 0 \\ \hline
**OUTPUT** & 0.224 & 0.274 & 0.134 & 0.368 & **OUTPUT** & 0.132 & 0.198 & 0.089 & 0.581 & **OUTPUT** & 0.054 & 0.109 & 0.040 & 0.797 \\ \hline
**INPUT** & 0 & 1 & 0 & 0 & **INPUT** & 0 & 1 & 0 & 0 & **INPUT** & 0 & 1 & 0 & 0 \\ \hline
**OUTPUT** & 0.251 & 0.524 & 0.056 & 0.169 & **OUTPUT** & 0.133 & 0.670 & 0.003 & 0.193 & **OUTPUT** & 0.063 & 0.837 & 0.000 & 0.100 \\ \hline
**INPUT** & 0 & 0 & 1 & 0 & **INPUT** & 0 & 0 & 1 & 0 & **INPUT** & 0 & 0 & 1 & 0 \\ \hline
**OUTPUT** & 0.169 & 0.056 & 0.505 & 0.271 & **OUTPUT** & 0.193 & 0.003 & 0.670 & 0.133 & **OUTPUT** & 0.100 & 0.000 & 0.837 & 0.063 \\ \hline
**INPUT** & 0 & 0 & 0 & 1 & **INPUT** & 0 & 0 & 0 & 1 & **INPUT** & 0 & 0 & 0 & 1 \\ \hline
**OUTPUT** & 0.368 & 0.134 & 0.271 & 0.227 & **OUTPUT** & 0.581 & 0.089 & 0.195 & 0.135 & **OUTPUT** & 0.796 & 0.041 & 0.108 & 0.055 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the simulation outputs of the mathematical model for 1-digit, 4-bit numbers and for different SJR values: (a) SJR = 0.7\(-\)0.3, (b) SJR = 0.8\(-\)0.2, and (c) SJR = 0.9\(-\)0.1. Figure 4: Represents the evolution of the intensities in the three layers with respect to the two input configurations, \(1-0-0-0\) and \(0-0-0-1\) (1-digit, 4-bit numbers), for the three cases of SJR: (**a**) SJR = \(0.7-0.3\); (**b**) SJR = \(0.8-0.2\); (**c**) SJR = \(0.9-0.1\). Figure 5: Represents the energy flow in a three-layer network learning two input numbers, \(1-0-0-1\) and \(0-1-1-0\), for the three SJRs: (**a**) SJR = \(0.7-0.3\); (**b**) SJR = \(0.8-0.2\); (**c**) SJR = \(0.9-0.1\). The SNN learning map evolution has been investigated also in the case of 4-bit numbers constituted by two ones and two zeros (2-digit, 4-bit numbers). Table 3 reports the output patterns for each input permutation, while Figure 5 represents the signals' flow in all channels. Each evolution, connected to a precise input pattern, is characterized by a specific signal pattern that becomes more and contrasted as the SJR values increase. This is indeed expected and will be also more evident with the solitonic simulations. Finally, the 4-bit SNN is capable of efficiently recognizing and storing patterns triggered by three input signals as well (3-digit, 4-bit numbers). Table 4 reports the learning  
Figure 6: Represents the evolution of the intensities in the three layers with respect to the three input configurations, \(1\!-\!1\!-\!1\!-\!0\) and \(0\!-\!1\!-\!1\!-\!1\), for the three cases of imbalance: (**a**) SJR = \(0.7\!-\!0.3\); (**b**) SJR = \(0.8\!-\!0.2\); (**c**) SJR = \(0.9\!-\!0.1\).