## 1 Introduction  
Over the last few decades, numerous studies have accumulated a great deal of knowledge about brain function, but our understanding of brain mechanisms and functional dynamics remains limited [1, 2]. Spiking neural networks (SNNs) have been increasingly popular in recent years due to their relationship to dynamics in human brain and enhanced biological relevance [3]. The SNNs with cognitive and motor functions, such as the hippocampal network, have been investigated with abundant in vitro and model-based experiments [4, 5]. One of the most essential components of the human brain is the hippocampus, which processes short-term memory and spatial navigation information [6, 7, 8, 9]. It can be biologically demonstrated that behavioral learning and memory in mammals are closely related to its hippocampal rhythm [10, 11]. In addition, there is ample evidence that abnormal hippocampal rhythms in animals induce psychological dysfunction of the nervous system. One of the most representational examples is that abnormal electrical activity in the hippocampal neural network can cause cognitive decline and behavioral inhibition in Alzheimer's patients [12, 13, 14, 15]. Previous experiments have shown that hippocampal oscillations play an irreplaceable role both in the encoding and transmission of information [16]. The dynamics of neuromodulation can affect sensory processing and information processing in cognitive function [17; 18]. Most of the current spiking neural network models for machine learning application use the feedforward network architecture for the sake of object classification. However, the human brain does not execute the classification task most of the time. Besides, many critical brain areas for learning do not use a feedforward architecture. On the basis of these observations, we were inspired to present a novel model with recurrent network architecture for the neuromodulatory dynamics, which are critical in the cognitive and learning process in human brain. Although the hippocampal neural network has been investigated for a long time and applied in various fields widely, its computational process has some constraint. CPU characteristics lead to low clock rates and low memory bandwidth on traditional platforms. A high-performance computing platform in neuromorphic engineering is urgently needed. Various kinds of hardware designs have endeavored to realize the real-time computation of the human brain system in recent years. The analog neuromorphic system based on the very large scale integration (VLSI) can be more realistic and power efficient [19; 20; 21], but it has not been popularized due to its excessive resource consumption and long development time. The field programmable gate array (FPGA) has the characteristics of parallel computation, reconfigurable architecture, and distributed structure in a digital neuromorphic system [22; 23]. Previous studies have focused on using FPGAs to improve the performance of neuromorphic platforms, which present different realization methods with various applications [24; 25; 26; 27]. However, although this is important for information encoding and signal processing in the brain's cognitive system, few previous studies have considered the dynamic oscillations of the hippocampal neural network. In addition, how to effectively implement large-scale hippocampal networks with coupled synapses with random connections is a difficult challenge. Due to the shortcomings of on-chip network (NoC) technology [28], the digital implementation of the seahorse network we proposed earlier is limited in scalability and system performance [29]. Since the introduction of microchip technology, great progress has been made in its development, and large-scale on-chip system technology has been applied in more and more fields. As the architectures of the system-on-chip platform become more complex, NoC techniques are investigated and used to solve the problems of data transmission, large-scale computation, and complicated network topology [30].