神经元模型对比分析

Contrastive analysis of neuron model

近年来,生物神经元模型的建立与应用已经获得了越来越多的关注,逐渐成为神经科学的一个重要分支.神经元模型不仅在仿生学、存储器设计、逻辑运算、信号处理等方面有重大应用,对分析研究神经系统的动力学特性也具有重要意义.本文总结了自1907年第一个神经元模型建立以来的发展历程,归纳出17种最具代表性的数学模型,分为电导依赖模型和非电导依赖模型进行比较分析,重点展示包括最新神经芯片TrueNorth上的神经元在内的5种经典模型,分析其仿真特性,以及电路实现的需求,方便研究者根据具体需求选择和改进神经元模型.

In recent years,the modeling and application of biological neurons have gained more and more attention.By now,the research on neuron models has become one of the most important branches of neuroscience.Neuron models can be used in various areas,such as biomimetic applications,memory design,logical computing,and signal processing.Furthermore,it is significant to study the dynamic characteristics of neural system by using neuron models.In this paper,the historical development of neuron models is reviewed.The neuron models have experienced three development stages.In the pioneering stage,a group of scientists laid the experimental and theoretical foundation for later research.Then,the whole study started to blossom after the publication of Hodgkin-Huxley model.In the 1970 s and 1980 s,various models were proposed.One of the research focuses was the simulation of neural repetitive spiking.Since the 1990 s,researchers have paid more attention to setting up models that are both physiologically meaningful and computationally effective.The model put forward by Izhikevich E M has been proved to solve the problem successfully.Recently,IBM presented a versatile spiking neuron model based on 1272 ASIC gates.The model,both theoretically understandable and physically implementable,has been used as a basic building block in IBM＇s neuro-chip True North.In the paper,seventeen neuron models worth studying are listed.To give a more explicit explanation,these models are classified as two groups,namely conductance-dependent and conductance-independent models.The former group＇s goal is to model the electrophysiology of neuronal membrane,while the latter group is only to seek for capturing the input-output behavior of a neuron by using simple mathematical abstractions.The complexity and features of each model are illustrated in a chart,while the prominent repetitive spiking curves of each model are also exhibited.Five of the models are further detailed,which are the Hodgkin-Huxley model,the Integrate-and-fire model,the Fitzhugh-Nagumo model,the Izhikevich model,and the most recent model used by IBM in its neuro-chip True North.Finally,three questions are put forward at the end of the paper,which are the most important problems that today＇s researchers must consider when setting up new neuron models.In conclusion,the feasibility of physical implementation remains to be a challenge to all researchers.Through the aforementioned work,the authors aim to provide a reference for the study that follows,helping researchers to compare those models in order to choose the properest one.