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Abstract
The identification method of backpropagation (BP) neural network is adopted to approximate the mapping relation between input and output of neurons based on neural firing trajectory in this paper. In advance, the input and output data of neural model is used for BP neural network learning, so that the identified BP neural network can present the transfer characteristics of the model, which makes the network precisely predict the firing trajectory of the neural model. In addition, the method is applied to identify electrophysiological experimental data of real neurons, so that the output of the identified BP neural network can not only accurately fit the neural firing trajectories of neurons participating in the network training but also predict the firing trajectories and spike moments of neurons which are not involved in the training process with high accuracy.
Highlights
Large-scale, high-flow data acquisition is revolutionizing the field of neuroscience [1]
System identification of quantitative mathematical models has proved to be an essential tool in exploring this issue [3,4]
Detailed computational models are required to fit the model to electrophysiological records [5,6]
Summary
Large-scale, high-flow data acquisition is revolutionizing the field of neuroscience [1]. Artificial neuron network is a mathematical model inspired by the process of synaptic connection and information processing in the biological nervous system. It consists of several interconnected neuron nodes and connection weights. BP network is a multilayer feedforward network trained by the error backpropagation algorithm proposed by a team of scientists led by Rumelhart and McCelland [13,14,15] It is one of the most widely used neural network models [16,17,18]. The input and output trajectories of neurons are used as identification features, and the signal transmission process of real neurons is identified using BP neural network. The influence of the relevant parameters of the network on the identification is studied
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