## 4 Experimental Results
(**a**) The output of the 1 st excitatory neuron in the digital neuromorphic network (time scale is 20 \(\upmu\)s); (**b**) The output of the 48,000 th excitatory neuron in the digital neuromorphic network (time scale is 20 \(\upmu\)s); (**c**) The output of the 1 st inhibitory neuron in the digital neuromorphic network (time scale is 20 \(\upmu\)s); (**d**) The output of the 16,000 th inhibitory neuron in the digital neuromorphic network (time scale is 20 \(\upmu\)s). In our study, we use frequency domain analysis to reveal how many signals are in a given frequency band within a given frequency range. According to the previous research results, the wavelet transform can effectively complete digital image processing and signal compression. As shown in Figure 6a,b, modulation of extracellular oscillations with negative and positive DC stimuli resulted in suppression and promotion of mean emissivity, respectively. During symmetrical modulation of low-frequency AC stimulation, the average activation rate of the hippocampal neural network increased, and the frequency of low-frequency AC stimulation was less than half (about 12 Hz) of the endogenous magnetic field frequency. In practice, symmetric modulation means that the effect of suppression is roughly the same as the effect of enhancement under stimulation of a low-frequency ac field. The suppressive neurons due to the effect of the negative cycles can fire during the positive cycles of the AC stimulation, which induces a stable network firing rate. Synchrony occurs during the emergency of subharmonic as shown in Figure 6c,d. Figure 5: Performance analysis of the proposed CORDIC module. (**a**) The relationship of the computational precision, the iteration number, and the number of the CORDIC modules; (**b**) The relationship between the number of CORDIC modules and the hardware resource cost. Previously we have presented several works for brain-inspired neuromorphic computing. In order to clarify the unique contribution in this study, a comparison with analysis is presented as illustrated in Table 3. These works have been inspired by different areas of the brain, which includes visual pathway, hippocampus, CPG (central pattern generator), Purkinje, and retina. Although [33] shares the same brain area with the presented study, different network structures with different aims are pursued. Study [33] uses the feedforward network structure for the implementation of the memory-related behaviors. This study presents a unique contribution for the realization of the neuromodulatory dynamics. In addition, this study presents a torus-based NoC design for hardware architecture, which presents another significant contribution in comparison with the previous studies. A previous study has revealed the advantage and necessity of the NoC design [28]. The presented study is the improved version in comparison with our previous studies [34; 35]. The previous studies [34; 35] use a bus-based NoC architecture. This work proposes a torus-based solution, which can further improve the NoC performance of the hippocampal network on FPGA, which is the major innovation and difference in comparison with our previous work. The weakness of the presented digital neuromorphic model is that it can only reproduce parts of the critical dynamics of the biological hippocampus. It cannot reproduce all the dynamics of the hippocampus region such as navigation, which should be further explored in a future study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Motivation** & **Network Structure** & **Hardware Architecture** \\ \hline
[27] & Visual pathway-inspired & Feedforward & No NoC design \\
[34] & Hippocamp-inspired & Feedforward & No NoC design \\
[36] & CPG-inspired & CPG & No NoC design \\
[37] & Purkinje-inspired & Recurrent & No NoC design \\
[38] & Retina-inspired & Feedforward & No NoC design \\ This study & Hippocamp-inspired & Recurrent & Torus-based NoC design \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the presented study with previous works.