# A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data
## 2 Related Works
### Spiking Neural Network  
Spiking neural network is called the third-generation artificial neural network, which is a discrete bionic network model that is closest to real life. Traditional artificial neural networks are still limited to the von Neumann architectures for information processing and learning [23]. In the von Neumann architecture, the memory and the processor are separated from each other, resulting in a large amount of energy being consumed when performing massive data operations [24]. However, spiking neural network encodes information into spike sequences for processing, that is, the input information is processed and transmitted through direct action potentials at synapses. It adopts mechanisms such as plastic synapses and spiking time coding to simulate the spatiotemporal properties of neural networks, and its structure is closer to biological neurons. Therefore, it has higher biological feasibility and more computational power than traditional neural networks [25]. The theory of spiking neural network can find the corresponding biological basis, which makes it have both good biological interpretability and the information in the time dimension. Compared with traditional neural networks, spiking neural network has more efficient computing power and is relatively easy to implement in hardware. Because their event-driven nature can reduce the power consumption of operations [26]. The excellent performance and advantages of spiking neural networks have attracted a large number of internationally renowned teams for long-term in-depth research [27; 28]. The main research directions of spiking neural network are discussed as follows. * Neurons. Biological neurons generally simulate their functions through neuron models. The neuron model is the basis for building spiking neural network. Different types of neurons are connected to each other to form various types of neural network models. Common spiking neuron models include Izhikevich model, HH (Hodgkin and Huxley model), LIF (Leaky Integrate and Fire Model), SRM (spike response model), etc. Among them, LIF and SRM are the most commonly used learning algorithms. Many learning algorithms are designed and implemented on the basis of these neuron models or their variants. * Network Topology. The topology of a neural network includes the number of network layers, the number of neurons in each layer, and the way each neuron connected to each other. The topology of artificial neural network is often divided into input layer, hidden layer and output layer, and each layer is connected in sequence. Among them, the neurons of input layer are responsible for receiving input information from the outside world and passing it to the neurons of hidden layer. The hidden layer is responsible for information processing and information transformation within the neural network. Usually, the hidden layer is designed as one or more layers according to the needs of transformation. Like the topology of traditional artificial neural network, the structure of spiking neural network mainly includes feedforward spiking neural network, recurrent spiking neural network and hybrid spiking neural network, etc. * Spike sequence encoding. For the encoding of input information, researchers have proposed a variety of spike sequence encoding methods for spiking neural networks by learning the information encoding mechanism of biological neurons. For example, the first spike-triggered time coding method, the delayed phase coding method, the population coding method, etc. * Learning algorithm. Spiking neural network contains hyperparameters such as network topology, number of neurons, and weights. During training network, these hyperparameters are determined by a learning algorithm. The learning algorithm directly determines the output accuracy of the spiking neural network.