基于FPGA的Hodgkin-Huxley神经元硬件实现
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- 英文论文题名：Realization of Hodgkin-Huxley Neuron by Employing FPGA Hardware
- 论文作者：王金龙 ; 逯迈 ; 陈小强
- 英文论文作者：WANG Jin-long ; LU Mai ; CHEN Xiao-qiang ; School of Automation & Electric Engineering ; Lanzhou Jiaotong University ; Key Lab of Optoelectronic Technology and Intelligent Control ; Ministry of Education ; Lanzhou Jiaotong University
- 年：2015
- 作者机构：兰州交通大学自动化与电气工程学院;兰州交通大学光电技术与智能控制教育部重点实验室;
- 论文关键词：Hodgkin-Huxley模型 ; FPGA ; DSP Builder ; 动作电位
- 英文论文关键词：Hodgkin-Huxley model ; FPFA ; DSP Builder
- 会议召开时间：2015-08-12
- 会议录名称：静电放电：从地面新技术应用到空间卫星安全防护—中国物理学会第二十届全国静电学术会议论文集
- 语种：中文
- 分类号：TN791;Q42
- 学会代码：WLJD
- 会议名称：静电放电 ; 从地面新技术应用到空间卫星安全防护—中国物理学会第二十届全国静电学术会议
- 会议地点：中国河北石家庄
- 主办单位：中国物理学会静电专业委员会
- 学会名称：中国物理学会静电专业委员会
- 页数：6
- 文件大小：773k
- 原文格式：D
- 会议级别：全国
摘要
本文研究利用FPGA硬件实现Hodgkin-Huxley(HH)神经元的新方法.首先对HH神经元的特性进行数值模拟分析;其次采用DSP Builder与Quartus II联合设计的方法,将HH神经元在FPGA上予以实现,并对不同刺激信号作用下FPGA实现的HH神经元动作电位与数值模拟结果进行了对比。利用FPGA可以很好地实现HH神经元的动作电位,对于相同的刺激信号,FPGA实现的HH神经元动作电位响应与数值模拟结果完全一致。得出结论:通过FPGA可以硬件实现HH模型神经元。
To find the new way for hardware implementation of Hodgkin-Huxley(HH)neuron in FPGA.First,the HH neuron was numerically analysed.Second,the HH neuron was implemented on FPGA by
employing the software such as DSP Builder and Quartus II simultaneously.The obtained action potentials on FPGA using different external stimulating signals were compared to those obtained by numerical
simulations.The action potential of HH neuron can be well realized on FPGA.The obtained action
potentials on FPGA using different external stimulating signals are well in agreement with those obtained by numerical simulations.It is feasible to realize hardware implementation of HH neuron on FPGA
To find the new way for hardware implementation of Hodgkin-Huxley(HH)neuron in FPGA.First,the HH neuron was numerically analysed.Second,the HH neuron was implemented on FPGA by
employing the software such as DSP Builder and Quartus II simultaneously.The obtained action potentials on FPGA using different external stimulating signals were compared to those obtained by numerical
simulations.The action potential of HH neuron can be well realized on FPGA.The obtained action
potentials on FPGA using different external stimulating signals are well in agreement with those obtained by numerical simulations.It is feasible to realize hardware implementation of HH neuron on FPGA
引文
[1]Hodgkin 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.
[2]Hodgkin A L,Huxley A F.The components of membrane conductance in the giant axon of Loligo[J].Journal of Physiology,1952,116(4):473-496.
[3]Hodgkin A L,Huxley A F.The dual effect of membrane potential on sodium conductance in the giant axon of Loligo[J].Journal of Physiology,1952,116(4):497-506.
[4]Hodgkin A L,Huxley A F.A quantitative description of membrane current and its application to conduction and excitation in nerve[J].Journal of Physiology,1952,117(4):500-544.
[5]Bazso F,Zalanyi L,Csardi G.Channel noise in Hodgkin-Huxley model neurons[J].Physics Letters A,2003,311(1):13-20.
[6]Nabi A,Moehlis J.Single input optimal control for globally coupled neuron networks[J].Journal of Neural Engineering,2011,8(6):065008.
[7]于海涛,王江,刘晨等.耦合小世界神经网络的随机共振[J].物理学报,2012,61(6):485-491.
[8]Doruk R O.Control of repetitive firing in Hodgkin-Huxley nerve fibers using electric fields[J].Chaos Solitons Fractals,2013,52:66-72.
[9]Yaghini Bonabi S,Asgharian H,Safari S,Nili Ahmadabadi M.FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model[J].Frontiers in Neuroscience,2014,8:379
[10]Grassia F,Levi T,Kohno T,Sa?ghi S.Silicon neuron:digital hardware implementation of the quartic model[J].Artificial Life and Robotics,2014,19(3):215-219.
[11]Labib R,Audette F,Fortin A,Assadi R.Hardware implementation of a new artificial neuron[J].International Journal of Neural Systems,2005,15(6):427-433.
[12]Tigaeru L,Bonternu G.A Neuron Model for FPGASpiking Neuronal Network Implementation[J].Aavances in Electrical and Computer Engineering,2011,11(4):29-36.
[13]陈军,李春光.具有自适应反馈突触的神经元模型中的混沌:电路设计[J].物理学报,2011,60(5):77-84.
[14]Dabrowski D,Jamro E,Cioch W.Hardware Implementation of Artificial Neural Networks for Vibroacoustic Signals Classification[J].Acta Physica Polonica A,2010,118(1):41-44.
[15]Aleksander I,Morton H.ANALOG VLSI ANDNEURAL SYSTEMS-MEAD,C[J].Nature,1989,340(6230):198.
[16]Mahowald M,Douglas R.A silicon neuron[J].Nature,1991,354(6354):515-518.
[17]Toumazou C,Drakakis E M,Georgiou J.Current-mode analogue circuit representation of Hodgkin and Huxley neuron equations[J].Electronics Letters,1998,34(14):1376-1377
[18]Folowosele F,Hamilton T J,Etienne-Cummings R.Silicon Modeling of the Mihalas-Niebur Neuron[J].IEEETransactions on Neural Networks,2011,22(12):1915-1927.
[19]Graas E L,Brown E,Lee R H.An FPGA-based approach to high-speed simulation of conductance-based neuron models[J].Neuroinformatics,2004,2(4):417-435.
[20]Weinstein R K,Lee R H.Architectures for high-performance FPGA implementations of neural models[J].Journal of Neural Engineering,2006,3:21-34.
[21]Weinstein R K,Reid M S,Lee R H.Methodology and design flow for assisted neural model implementations in FPGAs[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2007,15(1):83-93.
[22]张荣华,王江.FPGA在生物神经系统模型仿真中的应用[J].计算机应用研究,2011,28(8):2949-2953.
[23]张荣华,王江.神经元网络的FPGA硬件仿真方法[J].计算机应用研究,2011,28(10):3707-3710.
[24]FITZHUGH R,Impulses and physiological states in theoretical models of nerve membrane[J].Biophysical Journal,1961,1(6):445-466.
[25]HINDMARSH J L,ROSE R M.A model of neuronal bursting using three coupled first order differential equations[J].Proc.R.Soc.London Ser.B,1984,221(1222):87-102.
[26]MORRIS C,LECAR H.Voltage oscillations in the barnacle giant muscle fiber[J].Biophysics Journal,1981,35(1):193-213.
[2]Hodgkin A L,Huxley A F.The components of membrane conductance in the giant axon of Loligo[J].Journal of Physiology,1952,116(4):473-496.
[3]Hodgkin A L,Huxley A F.The dual effect of membrane potential on sodium conductance in the giant axon of Loligo[J].Journal of Physiology,1952,116(4):497-506.
[4]Hodgkin A L,Huxley A F.A quantitative description of membrane current and its application to conduction and excitation in nerve[J].Journal of Physiology,1952,117(4):500-544.
[5]Bazso F,Zalanyi L,Csardi G.Channel noise in Hodgkin-Huxley model neurons[J].Physics Letters A,2003,311(1):13-20.
[6]Nabi A,Moehlis J.Single input optimal control for globally coupled neuron networks[J].Journal of Neural Engineering,2011,8(6):065008.
[7]于海涛,王江,刘晨等.耦合小世界神经网络的随机共振[J].物理学报,2012,61(6):485-491.
[8]Doruk R O.Control of repetitive firing in Hodgkin-Huxley nerve fibers using electric fields[J].Chaos Solitons Fractals,2013,52:66-72.
[9]Yaghini Bonabi S,Asgharian H,Safari S,Nili Ahmadabadi M.FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model[J].Frontiers in Neuroscience,2014,8:379
[10]Grassia F,Levi T,Kohno T,Sa?ghi S.Silicon neuron:digital hardware implementation of the quartic model[J].Artificial Life and Robotics,2014,19(3):215-219.
[11]Labib R,Audette F,Fortin A,Assadi R.Hardware implementation of a new artificial neuron[J].International Journal of Neural Systems,2005,15(6):427-433.
[12]Tigaeru L,Bonternu G.A Neuron Model for FPGASpiking Neuronal Network Implementation[J].Aavances in Electrical and Computer Engineering,2011,11(4):29-36.
[13]陈军,李春光.具有自适应反馈突触的神经元模型中的混沌:电路设计[J].物理学报,2011,60(5):77-84.
[14]Dabrowski D,Jamro E,Cioch W.Hardware Implementation of Artificial Neural Networks for Vibroacoustic Signals Classification[J].Acta Physica Polonica A,2010,118(1):41-44.
[15]Aleksander I,Morton H.ANALOG VLSI ANDNEURAL SYSTEMS-MEAD,C[J].Nature,1989,340(6230):198.
[16]Mahowald M,Douglas R.A silicon neuron[J].Nature,1991,354(6354):515-518.
[17]Toumazou C,Drakakis E M,Georgiou J.Current-mode analogue circuit representation of Hodgkin and Huxley neuron equations[J].Electronics Letters,1998,34(14):1376-1377
[18]Folowosele F,Hamilton T J,Etienne-Cummings R.Silicon Modeling of the Mihalas-Niebur Neuron[J].IEEETransactions on Neural Networks,2011,22(12):1915-1927.
[19]Graas E L,Brown E,Lee R H.An FPGA-based approach to high-speed simulation of conductance-based neuron models[J].Neuroinformatics,2004,2(4):417-435.
[20]Weinstein R K,Lee R H.Architectures for high-performance FPGA implementations of neural models[J].Journal of Neural Engineering,2006,3:21-34.
[21]Weinstein R K,Reid M S,Lee R H.Methodology and design flow for assisted neural model implementations in FPGAs[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2007,15(1):83-93.
[22]张荣华,王江.FPGA在生物神经系统模型仿真中的应用[J].计算机应用研究,2011,28(8):2949-2953.
[23]张荣华,王江.神经元网络的FPGA硬件仿真方法[J].计算机应用研究,2011,28(10):3707-3710.
[24]FITZHUGH R,Impulses and physiological states in theoretical models of nerve membrane[J].Biophysical Journal,1961,1(6):445-466.
[25]HINDMARSH J L,ROSE R M.A model of neuronal bursting using three coupled first order differential equations[J].Proc.R.Soc.London Ser.B,1984,221(1222):87-102.
[26]MORRIS C,LECAR H.Voltage oscillations in the barnacle giant muscle fiber[J].Biophysics Journal,1981,35(1):193-213.