## 2 The Hippocampal Neural Network Model  
As shown in Figure 1, hippocampal neural coding is closely related to peridergic pyramidal neurons and inhibitory neurons, and the hippocampal neural network can contain both important types of neurons. Pyramidal neurons are asymmetrical in the narrow region between the dendrites and the cell body, whereas the structures of the genus rabbit on both sides of the cell body of the intermediate neuron are symmetrical. As a result, the two types of neurons have different effects in the electrical field outside the cell. Besides, the network is coupled with various synaptic connections due to the relatively local dense connectivity. In this paper, Ne = 48,000 excitatory neurons and Ni = 16,000 inhibitory neurons were used to form a hippocampal network model, and the synapses between these neurons were connected with a sparsity of 20%. When neuron \(i\) peaks, the synaptic current of neuron \(j\) is weighted by the presynaptic pulse \(s(i,t)\), which is weighted by the corresponding synaptic current \(w(j,i)\). This current generation process can be regarded as the integral process of all synaptic current to the \(j\)th neuron, which can be defined by the following mathematical equation:  
\[\partial I_{S}(j)/\partial t=\left(-I_{S}(j)+\sum_{i}g_{syn}w(j,i)s(i,t) \right)/\tau_{S}(j) \tag{1}\]  
In the above formula, \(s(i,t)\) is used to indicate presynaptic impulse, and \(w(j,t)\) is used to describe synaptic strength. For different connections between excitatory neurons and inhibitory neurons, the parameter range of synaptic strength is different: the synaptic strength w is uniformly distributed with \(w_{ox}\in[0,0.65]\) for: excitatory-to-excitatory synapses, \(w_{oi}\in[0,2]\) for excitatory-to-inhibitory synapses, \(w_{iv}\in[-1.7,-0.8]\) for inhibitory-to-excitatory synapses, and \(w_{ii}\in[-1.1,-0.3]\) for inhibitory-to-inhibitory synapses. \(\tau_{s}\) is a constant describing the synapse time constant. For excitatory neurons, \(\tau_{s}\) = 0.5 ms, and for inhibitory neurons, \(\tau_{s}\) = 6 ms. The effective gain \(g_{syn}\), which stands for the amplitude response of the synaptic current neurons, is \(8.7\pm 4.8\) Hz and \(21.1\pm 1.4\) Hz for inhibitory neurons [31]. The firing rate of the excitatory field \(E\) will affect the nerve polarization of cone excitatory neurons, and \(E\) is proportional to the applied electric field, which can be described by the following mathematical equation:  
\[\partial I_{E}/\partial t=\left(-I_{E}+S_{E}E(t)\right)/\tau_{E} \tag{2}\]  
The \(S_{E}\) in the above formula is a description of the sensitivity of the membrane to the field determined by the cell geometry and field orientation. It should be noted that the parameter \(\tau_{E}\) in the formula is a constant value and will not change with the iteration process. At the same time, the total input current \(l\) of the \(j\)th neuron is proposed, which is expressed as follows:  
Figure 1: The overall structure of hippocampus and the hippocampal network in the CA3 region. The hippocampal network consists of pyramidal excitatory neurons and inhibitory neurons. These two types of neurons are coupled to each other through excitatory and inhibitory synapses with a variety of synaptic strengths. \[I(j)=\left\{ \begin{array}{ll}I_{S}(j)+I_{E}+I_{car},&\text{for $j$th excitatory neuron}\\ I_{S}(j),&\text{for $j$th inhibitory neuron}\end{array} \right. \tag{3}\]  
In this paper, in order to describe the impact caused by carbachol, \(I_{car}\) uses a Gaussian white noise current with an average value of 0. The advantage of this choice is that the operation of the endogenous hippocampal neural network can be demonstrated by applying a weak electric field. Through the past research on neural network models, it can be found that using excitatory neurons and inhibitory neurons to build a hippocampal neural network model can achieve very good simulation results.