神经信息传导的电路模型
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摘要
神经系统是由大量神经元耦合形成的复杂网络,各神经元之间主要通过膜电压和突触电流实现神经信息的传导。由于通用计算机计算性能的限制,软件仿真无法满足研究复杂的生物神经元网络的要求;数字专用集成电路芯片无需读写程序即可并行完成多个数学运算,将其应用于神经系统的电路建模中可显著提高计算效率。因此建立神经信息传导的电路模型是研究神经系统电生理特性的有效方法之一。本文研究了基于现场可编程门阵列(FPGA)技术的神经电信息传导电路模型,并通过对神经元及神经网络的分析证明了模型的有效性。
本文基于流水线思想,实现了在FPGA上同时求解多个常微分方程的流水线数据通路方法。首先利用流水线算子的概念实现了从常微分方程到流水线数据通路的严格映射关系,建立了在FPGA上求解常微分方程的多进程流水线模型;然后通过分析多进程流水线模型上各状态变量在时间和空间上的分布关系,推导出了保持所模拟的神经元模型与真实的生物神经元模型在时间尺度上一致的条件,给出了利用流水线数据通路求解耦合的多个常微分方程的方法。
本文还设计了基于FPGA技术的神经元网络电路实验平台,该平台由FPGA的计算核心,USB数据上传系统以及AD,DA转换电路组成。根据AD和DA转换过程中的延时对系统实时性的影响问题,提出了两种时序控制方案,保证了模型在神经元数学模型的一个迭代步内完成AD和DA的数据转换,维持了系统的实时性。
本文根据Morris-Lecar神经元以及由该神经元通过化学突触组成的神经元网络的电生理特性,利用多进程流水实现了Morris-Lecar神经元及网络的FPGA模型,并利用FPGA电路模型分析了Morris-Lecar神经元的放电模式、耦合神经元同步等动力学特性,证明了方法的正确性和可行性。
海马是大脑决定认知、情感等重要脑功能的主要神经组织,本文法实现了海马CA3区椎体神经元及网络的FPGA电路模型,分析了海马CA3区椎体神经元的动力学特性,模拟了两个海马神经元同步放电现象。
本文的工作是研究神经元模型的重要手段,通过数字电路准确、快速的再现生物神经系统丰富的动力学现象,可以为针刺电信息传导的规律研究提供可重复的模拟实验对象。
Nervous system is a complex network composed by a large number of neurons which conduct the neural information mainly through the membrane voltage and synaptic current. Constrained by the performance of general-purpose computer, software simulation can not satisfy the biological neural network research requirements. Instead of reading the program, digital ASIC devices can complete the computation in parallel mathematics. Implementing the electrical model of nervous system with digital ASIC can improve the computational efficiency significantly. It is an effective method to study the electrophysiological properties of the nervous system. Based on field programmable gate array (FPGA) technology, the electrical model of neural information conduction was built in this paper, and the experimental results show the validity of the model.
Based on the pipeline idea, the data path in the FPGA can solve multiple ordinary differential equations simultaneously. Firstly, in this paper, the strict mapping between the pipeline data path and the ordinary differential equation was established by the method of pipeline operator, and the Multi-Process Pipeline Model of ordinary differential equation was built. Secondly, the necessary condition to maintain the neural model and biological neurons in the same time scale was deduced by analyzing the time and space distribution relationship of the state variables in the Multi-Process Pipeline Model. Finally, the solution to implement the coupling ordinary differential equations with pipeline data path was given.
In this paper, an experimental platform based on FPGA technology was designed. It was composed of FPGA computing core; USB data upload system and the AD, DA conversion circuit. The delay of the AD and DA conversion process will impact the real-time nature of the platform. Two sequential control methods were proposed to ensure that the AD and DA processes can be completed in one iterative step.
According to the electrophysiological properties of Morris-Lecar neurons and the neural network, the electrical model of Morris-Lecar neurons and the network was implemented with the Multi-Process Pipeline Model. The firing patterns and synchronization phenomena were analyzed through the electrical model, indcating the correctness and feasibility of the method.
Hippocampus is an important brain tissue to cognitive, emotional and other important brain functions. The electrical model of hippocampus neurons in CA3 area was built by the Multi-Process Pipeline Model mehtod in this paper. The electrophysiological properties of the hippocampus neuron were analyzed by the electrical and the results show that the synchronization phenomenon exists in the hippocampus neural network.
An effective means to research the production and conduction process of neural information was proposed in this paper. The dynamics of biological nervous system can be implemented quickly and accurately with the electrical model. It supplies a recoverable experimental subject for the research of acupuncture information conduction.
本文基于流水线思想,实现了在FPGA上同时求解多个常微分方程的流水线数据通路方法。首先利用流水线算子的概念实现了从常微分方程到流水线数据通路的严格映射关系,建立了在FPGA上求解常微分方程的多进程流水线模型;然后通过分析多进程流水线模型上各状态变量在时间和空间上的分布关系,推导出了保持所模拟的神经元模型与真实的生物神经元模型在时间尺度上一致的条件,给出了利用流水线数据通路求解耦合的多个常微分方程的方法。
本文还设计了基于FPGA技术的神经元网络电路实验平台,该平台由FPGA的计算核心,USB数据上传系统以及AD,DA转换电路组成。根据AD和DA转换过程中的延时对系统实时性的影响问题,提出了两种时序控制方案,保证了模型在神经元数学模型的一个迭代步内完成AD和DA的数据转换,维持了系统的实时性。
本文根据Morris-Lecar神经元以及由该神经元通过化学突触组成的神经元网络的电生理特性,利用多进程流水实现了Morris-Lecar神经元及网络的FPGA模型,并利用FPGA电路模型分析了Morris-Lecar神经元的放电模式、耦合神经元同步等动力学特性,证明了方法的正确性和可行性。
海马是大脑决定认知、情感等重要脑功能的主要神经组织,本文法实现了海马CA3区椎体神经元及网络的FPGA电路模型,分析了海马CA3区椎体神经元的动力学特性,模拟了两个海马神经元同步放电现象。
本文的工作是研究神经元模型的重要手段,通过数字电路准确、快速的再现生物神经系统丰富的动力学现象,可以为针刺电信息传导的规律研究提供可重复的模拟实验对象。
Nervous system is a complex network composed by a large number of neurons which conduct the neural information mainly through the membrane voltage and synaptic current. Constrained by the performance of general-purpose computer, software simulation can not satisfy the biological neural network research requirements. Instead of reading the program, digital ASIC devices can complete the computation in parallel mathematics. Implementing the electrical model of nervous system with digital ASIC can improve the computational efficiency significantly. It is an effective method to study the electrophysiological properties of the nervous system. Based on field programmable gate array (FPGA) technology, the electrical model of neural information conduction was built in this paper, and the experimental results show the validity of the model.
Based on the pipeline idea, the data path in the FPGA can solve multiple ordinary differential equations simultaneously. Firstly, in this paper, the strict mapping between the pipeline data path and the ordinary differential equation was established by the method of pipeline operator, and the Multi-Process Pipeline Model of ordinary differential equation was built. Secondly, the necessary condition to maintain the neural model and biological neurons in the same time scale was deduced by analyzing the time and space distribution relationship of the state variables in the Multi-Process Pipeline Model. Finally, the solution to implement the coupling ordinary differential equations with pipeline data path was given.
In this paper, an experimental platform based on FPGA technology was designed. It was composed of FPGA computing core; USB data upload system and the AD, DA conversion circuit. The delay of the AD and DA conversion process will impact the real-time nature of the platform. Two sequential control methods were proposed to ensure that the AD and DA processes can be completed in one iterative step.
According to the electrophysiological properties of Morris-Lecar neurons and the neural network, the electrical model of Morris-Lecar neurons and the network was implemented with the Multi-Process Pipeline Model. The firing patterns and synchronization phenomena were analyzed through the electrical model, indcating the correctness and feasibility of the method.
Hippocampus is an important brain tissue to cognitive, emotional and other important brain functions. The electrical model of hippocampus neurons in CA3 area was built by the Multi-Process Pipeline Model mehtod in this paper. The electrophysiological properties of the hippocampus neuron were analyzed by the electrical and the results show that the synchronization phenomenon exists in the hippocampus neural network.
An effective means to research the production and conduction process of neural information was proposed in this paper. The dynamics of biological nervous system can be implemented quickly and accurately with the electrical model. It supplies a recoverable experimental subject for the research of acupuncture information conduction.
引文
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