Wang Tian, Wang Ye, Shen Jiamin, Wang Lei, Cao Lihong
State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China.
Neuroscience and Intelligent Media Institute, Communication University of China, Beijing, China.
Front Comput Neurosci. 2022 Feb 7;15:800875. doi: 10.3389/fncom.2021.800875. eCollection 2021.
Hodgkin-Huxley (HH)-type model is the most famous computational model for simulating neural activity. It shows the highest accuracy in capturing neuronal spikes, and its model parameters have definite physiological meanings. However, HH-type models are computationally expensive. To address this problem, a previous study proposed a spike prediction module (SPM) to predict whether a spike will take place 1 ms later based on three voltage values with intervals of 1 ms. Although SPM does well, it fails to evaluate the informative features of the spike. In this study, the feature prediction module (FPM) based on simple artificial neural network (ANN) was proposed to predict spike features including maximum voltage, minimum voltage, and dropping interval. Nine different HH-type models were adopted whose firing patterns cover most of the firing behaviors observed in the brain. Voltage and spike feature samples under constant external input current were collected for training and testing. Experiment results illustrated that the combination of SPM and FPM can accurately predict the spiking part of different HH-type models and can generalize to unseen types of input current. The combination of SPM and FPM may offer a possible way to simulate the action potentials of biological neurons with high accuracy and efficiency.
霍奇金-赫胥黎（HH）型模型是模拟神经活动最著名的计算模型。它在捕捉神经元尖峰方面具有最高的准确性，其模型参数具有明确的生理意义。然而，HH型模型计算成本高昂。为了解决这个问题，之前的一项研究提出了一个尖峰预测模块（SPM），基于间隔为1毫秒的三个电压值来预测1毫秒后是否会出现尖峰。尽管SPM表现良好，但它未能评估尖峰的信息特征。在本研究中，提出了基于简单人工神经网络（ANN）的特征预测模块（FPM），以预测尖峰特征，包括最大电压、最小电压和下降间隔。采用了九种不同的HH型模型，其放电模式涵盖了大脑中观察到的大部分放电行为。收集了恒定外部输入电流下的电压和尖峰特征样本用于训练和测试。实验结果表明，SPM和FPM的组合可以准确预测不同HH型模型的尖峰部分，并且可以推广到未见过的输入电流类型。SPM和FPM的组合可能为高精度、高效率地模拟生物神经元的动作电位提供一种可能的方法。
