系统仿真学报 ›› 2019, Vol. 31 ›› Issue (1): 1-7.doi: 10.16182/j.issn1004731x.joss.17-0213

• 仿真建模理论与方法 •    下一篇

基于突触电导计算分离的一种新的时钟同步算法

王直杰, 彭霞, 韩芳, 顾晓春   

  1. 东华大学 信息科学与技术学院,上海 201620
  • 收稿日期:2017-05-09 修回日期:2017-09-25 出版日期:2019-01-08 发布日期:2019-04-16
  • 作者简介:王直杰(1969-),男,浙江,博士,教授,研究方向为神经动力学与类脑计算;彭霞(1982-),女,湖南,博士生,工程师,研究方向为神经动力学与类脑计算。
  • 基金资助:
    国家自然科学基金面上项目(11572084)

New Clock-Driven Algorithm Based on Separation of Synaptic Conductance Computation

Wang Zhijie, Peng Xia, Han Fang, Gu Xiaochun   

  1. College of Information Science and Technology, Donghua University, Shanghai 201620, China
  • Received:2017-05-09 Revised:2017-09-25 Online:2019-01-08 Published:2019-04-16

摘要: 为降低生物神经网络仿真时间,提出一种基于突触电导计算分离的有效时钟同步算法。发现突触的仿真计算可分解为突触电导系数和突触电流两个独立计算部分;进一步引入“虚拟突触簇”数据结构存储突触电导系数序列,在每个仿真步根据突触前神经元放电状况单独计算突触电导系数并以循环数组结构保存,在计算以该神经元为突触前神经元的所有突触的突触电流时,只需从该数据结构中获取电导系数,从而大大减少了突触电导系数的重复计算。仿真结果表明了该算法的有效性。

关键词: 时钟同步算法, 突触电导, 突触电流, 神经网络

Abstract: In order to reduce the computing time when simulating the biologic neural network, an efficient clock-driven algorithm based on the separation of synaptic conductance computation is presented. It is found that the calculation of the synaptic state variables can be separated into two independent parts: one called conductance coefficient related with the pre-synaptic neuron, and the other called synaptic current. By introducing the data structure of the virtual synapse cluster to storing sequences of synaptic conductance coefficient, the former part can be calculated independently according to the spiking states of pre-synaptic neuron at each time step. When calculating the currents of all the synapses related with this pre-synaptic neuron, it is only need to calculate synaptic current by accessing the conductance coefficient from the virtual synapse cluster. Thus, the repetition of the calculation of the synaptic conductance coefficient is reduced. Simulation results validate the algorithm proposed in this paper.

Key words: clock-driven algorithm, synaptic conductance, synaptic current, neural network

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