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.	于海濤, 王江, 劉晨等.耦合小世界神經網絡的隨機共振[J].物理學報, 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.	陳軍, 李春光.具有自適應反饋突觸的神經元模型中的混沌:電路設計[J].物理學報, 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.	張榮華, 王江.FPGA在生物神經系統模型仿真中的應用[J].計算機應用研究, 2011, 28(8):2949-2953.
23.	張榮華, 王江.神經元網絡的FPGA硬件仿真方法[J].計算機應用研究, 2011, 28(10):3707-3710.
24.	謝明文.關于協方差、相關系數與相關性的關系[J].數理統計與管理, 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. 于海濤, 王江, 劉晨等.耦合小世界神經網絡的隨機共振[J].物理學報, 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. 陳軍, 李春光.具有自適應反饋突觸的神經元模型中的混沌:電路設計[J].物理學報, 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. 張榮華, 王江.FPGA在生物神經系統模型仿真中的應用[J].計算機應用研究, 2011, 28(8):2949-2953.
23. 張榮華, 王江.神經元網絡的FPGA硬件仿真方法[J].計算機應用研究, 2011, 28(10):3707-3710.
24. 謝明文.關于協方差、相關系數與相關性的關系[J].數理統計與管理, 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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