Hardware Implementation of Numerical Simulation Function of Hodgkin-Huxley Model Neurons Action Potential Based on Field Programmable Gate Array_Journal of Biomedical Engineering

Authors：

WANGJinlong ¹ ,  LUMai ² , HUYanwen ¹ , CHENXiaoqiang ¹ , PANQiangqiang ²

1. The School of Automatization & Electric Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China;
2. Key Lab of Opto-Electronic Technology and Intelligent Control of Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, China;

Corresponding author：

LUMai, Email: mai.lu@hotmail.com

Keywords：

Hodgkin-Huxley model; neuron; Field Programmable Gate Array (FPGA); action potential

DOI：

10.7507/1001-5515.20150231

Video：

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Neuron is the basic unit of the biological neural system. The Hodgkin-Huxley (HH) model is one of the most realistic neuron models on the electrophysiological characteristic description of neuron. Hardware implementation of neuron could provide new research ideas to clinical treatment of spinal cord injury, bionics and artificial intelligence. Based on the HH model neuron and the DSP Builder technology, in the present study, a single HH model neuron hardware implementation was completed in Field Programmable Gate Array (FPGA). The neuron implemented in FPGA was stimulated by different types of current, the action potential response characteristics were analyzed, and the correlation coefficient between numerical simulation result and hardware implementation result were calculated. The results showed that neuronal action potential response of FPGA was highly consistent with numerical simulation result. This work lays the foundation for hardware implementation of neural network.

Citation： WANGJinlong, LUMai, HUYanwen, CHENXiaoqiang, PANQiangqiang. Hardware Implementation of Numerical Simulation Function of Hodgkin-Huxley Model Neurons Action Potential Based on Field Programmable Gate Array. Journal of Biomedical Engineering, 2015, 32(6): 1302-1309. doi: 10.7507/1001-5515.20150231 Copy

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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, et al. 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, et al. 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, et al. 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 FPGA spiking 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.	MEAD C A. Analog VLSI and neural systems[M]. Reading.MA:Addison-Wesley, 1989.
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]. IEEE Transactions 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.	谢明文.关于协方差、相关系数与相关性的关系[J].数理统计与管理, 2004, 44(4):33-37.

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, et al. 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, et al. 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, et al. 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 FPGA spiking 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. MEAD C A. Analog VLSI and neural systems[M]. Reading.MA:Addison-Wesley, 1989.
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]. IEEE Transactions 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. 谢明文.关于协方差、相关系数与相关性的关系[J].数理统计与管理, 2004, 44(4):33-37.

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Hardware Implementation of Numerical Simulation Function of Hodgkin-Huxley Model Neurons Action Potential Based on Field Programmable Gate Array

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