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神经元网络的FPGA硬件仿真方法 被引量：5

Hardware simulation method for neural network with FPGA
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摘要 提出了一种利用FPGA对生物神经元网络进行硬件仿真的方法。该方法充分考虑了多进程流水线模型中各神经元状态输出的时序问题,设计了节点选择器完成对流水线数据通路输出数据的保存和选择功能,实现了多状态耦合情况下精确的仿真方法。最后,利用该方法对Morris-Lecar神经元网络模型进行了FPGA硬件仿真,再现了Morris-Lecar神经元网络的非线性动力学特性。 This paper proposed a method of biological neural network FPGA hardware simulation.The method fully considered the timing of each neuron state output in the multi-process pipeline model,designed the node selector to complete the pipeline data path output data saving and selection,and implemented the precise simulation of the multi-state coupling model.Finally,the methods implemented the FPGA hardware simulation of Morris-Lecar neural network model,and achieved the Morris-Lecar neural network＇s nonlinear dynamics.
作者 张荣华 王江
机构地区 天津大学电气与自动化工程学院
出处 《计算机应用研究》 CSCD 北大核心 2011年第10期3707-3710,共4页 Application Research of Computers
基金 国家自然科学基金资助项目(61072012)
关键词 现场可编程门阵列 流水线 生物神经元网络 field programmable gate array（FPGA） pipeline biological neuron network
分类号 TP33 [自动化与计算机技术—计算机系统结构]
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参考文献15

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二级参考文献15

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共引文献7

同被引文献41

  • 1陈春富,郎森阳.特发性氯离子通道缺陷所致神经肌肉病[J].中华神经科杂志,2004,37(4):360-361. 被引量：1
  • 2原亮,丁国良,吴文术,娄建安,赵强.以FPGA和QUARTUS为基础平台的EHW环境实现[J].计算机与数字工程,2006,34(5):1-3. 被引量：6
  • 3HODGKIN A L, HUXLEY A F. Current carried by sodium and potassium ions though the membrane of the giant axon of Loligo [J]. Journal of Physiology, 1952, 116(4): 449-472.
  • 4HODGKIN A L, HUXLEY A F. The components of mem- brane conductance in the giant axon of Loligo [J]. Journal of Physiology, 1952, 116(4) 473-496.
  • 5HODGKIN A L, HUXLEY A F. The dual effect of mem- brane potential on sodium conductance in the giant axon of Loligo [J]. Journal of Physiology, 1952, 116(4) : 497-506.
  • 6HOI3GKIN A L, HUXLEY A F. A quantitati,Je description of membrane current and its application to conduction and ex- citation in nerve . Journal of Physiology, 1952, 117(4): 500-544.
  • 7BAZSO F, ZALANYI L, CSARDI G. Channel noise in Hodgkin-Huxley model neurons [J]. Physics Letters A, 2003, 311(1): 13-20.
  • 8NAB1 A, MOEHLIS J. Single input optimal control for glob- ally coupled neuron networks [J]. Journal of Neural Engi- neering, 2011, 8(6): 065008.
  • 9DORUK R O. Control of repetitive firing in Hodgkin-Huxley nerve fibers using electric fields [J]. Chaos Solitons Fractals, 2013, 52: 66-72.
  • 10YAGHINI BONABI S, ASGHARIAN H, SAFARI S, et al. FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model [J]. Frontiers in Neuro- science, 2014, 8:379.

引证文献5

二级引证文献20

计算机应用研究
2011年 第10期

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