FPGA implementation of a biological neural network based on the hodgkin-huxley neuron model

Frontiers in Neuroscience

Volumn 8, Issue Nov, 2014, Pages

  Bonabi, Safa Yaghini ^a   Asgharian, Hassan ^b   Safari, Saeed ^a   Ahmadabadi, Majid Nili ^a,c  

^a UNIVERSITY OF TEHRAN   (Iran)

^b IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

^c INSTITUTE FOR STUDIES IN THEORETICAL PHYSICS AND MATHEMATICS IPM   (Iran)

Author keywords

Digital hardware implementation; FPGA; Hodgkin huxley; Neural network; Neural pool

Indexed keywords

VOLTAGE GATED CHANNEL FORMING PROTEIN;

ALGORITHM; ARITHMETIC; ARTICLE; COMPUTER; FIELD PROGRAMMABLE GATE ARRAY; HODGKIN HUXLEY EQUATION; INTEGRATED CIRCUIT; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; NERVE CELL; NERVE CELL NETWORK; NEUROPHYSIOLOGY; RESEARCH; ROBOTICS; SYNAPTIC EFFICACY; VALIDATION PROCESS;

EID: 84920693881     PISSN: 16624548     EISSN: 1662453X     Source Type: Journal    
DOI: 10.3389/fnins.2014.00379     Document Type: Article

References (35)

1. Ambroise, M., Levi, T., Joucla, S., Yvert, B., and Saïghi, S. (2013). Real-time biomimetic central pattern generators in an FPGA for hybrid experiments. Front. Neurosci. 7:215. doi: 10.3389/fnins.2013.00215
2. Bailey, J. A., Wilcock, R., Wilson, P. R., and Chad, J. E. (2011). Behavioral simulation and synthesis of biological neuron systems using synthesizable VHDL. Neurocomputing 74, 2392-2406. doi: 10.1016/j.neucom.2011.04.001
3. Bakhtiari, R., Sephavand, N. M., Ahmadabadi, M. N., Araabi, B. N., and Esteky, H. (2012). Computational model of excitatory/inhibitory ratio imbalance role in attention deficit disorders. J. Comput. Neurosci. 33, 389-404. doi: 10.1007/s10827-012-0391-y
4. Bonabi, S. Y., Asgharian, H., Bakhtiari, R., Safari, S., and Ahmadabadi, M. N. (2012a). "FPGA implementation of Hodgkin-Huxley neuron model," in IJCCI, eds A. C. Rosa, A. D. Correia, K. Madani, J. Filipe, and J. Kacprzyk (SciTePress), 522-528.
5. Bonabi, S. Y., Asgharian, H., Bakhtiari, R., Safari, S., and Ahmadabadi, M. N. (2012b). "FPGA implementation of a cortical network based on the Hodgkin-Huxley neuron model," in Neural Information Processing, eds T. Huang, Z. Zeng, C. Li, and C. S. Leung (Berlin; Heidelberg: Springer), 243-250.
6. Börgers, C., Epstein, S., and Kopell, N. J. (2005). Background gamma rhythmicity and attention in cortical local circuits: a computational study. Proc. Natl. Acad. Sci. U.S.A. 102, 7002-7007. doi: 10.1073/pnas.0502366102
7. Buzsáki, G. (2004). Large-scale recording of neuronal ensembles. Nat. Neurosci. 7, 446-451. doi: 10.1038/nn1233
8. Cassidy, A., and Andreou, A. G. (2008). "Dynamical digital silicon neurons," in IEEE Biomedical Circuits and Systems Conference, 2008. BioCAS 2008 (Baltimore, MD), 289-292. doi: 10.1109/BIOCAS.2008.4696931
9. Çavuşlu, M. A., Karakuzu, C., Şahin, S., and Yakut, M. (2011). Neural network training based on FPGA with floating point number format and it's performance. Neural Comput. Appl. 20, 195-202. doi: 10.1007/s00521-010-0423-3
10. Chen, K., and Wang, D. (2002). A dynamically coupled neural oscillator network for image segmentation. Neural Netw. 15, 423-439. doi: 10.1016/S0893-6080(02)00028-X
11. Ercegovac, M. D., and Lang, T. (2003). Digital Arithmetic. San Francisco, CA: Elsevier.
12. Ermentrout, G. B., and Kopell, N. (1998). Fine structure of neural spiking and synchronization in the presence of conduction delays. Proc. Natl. Acad. Sci. U.S.A. 95, 1259-1264. doi: 10.1073/pnas.95.3.1259
13. Gatet, L., Tap-Béteille, H., and Bony, F. (2009). Comparison between analog and digital neural network implementations for range-finding applications. IEEE Trans. Neural Netw. 20, 460-470. doi: 10.1109/TNN.2008.2009120
14. Graas, E. L., Brown, E. A., and Lee, R. H. (2004). An FPGA-based approach to high-speed simulation of conductance-based neuron models. Neuroinformatics 2, 417-435. doi: 10.1385/NI:2:4:417
15. Grassia, F., Lévi, T., Tomas, J., Renaud, S., and Saïghi, S. (2011). "A neuromimetic spiking neural network for simulating cortical circuits," in 45th Annual Conference on Information Sciences and Systems (CISS), 2011 (Baltimore, MD), 1-6. doi: 10.1109/CISS.2011.5766098
16. Heo, Y., and Song, H. (2012). Circuit modeling and implementation of a biological neuron using a negative resistor for neuron chip. BioChip J. 6, 17-24. doi: 10.1007/s13206-012-6103-x
17. Hodgkin, A. L., and Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500.
18. Indiveri, G., Linares-Barranco, B., Hamilton, T., VanSchaik, A., Etienne-Cummings, R., Delbruck, T., et al. (2011). Neuromorphic silicon neuron circuits. Front. Neurosci. 5:73. doi: 10.3389/fnins.2011.00073
19. Izhikevich, E. M. (2007). Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. Cambridge, MA: The MIT press.
20. Kandel, E. R., Schwartz, J. H., and Jessell, T. M. (2000). Principles of Neural Science. 4th Edn. New York, NY: McGraw-Hill Medical.
21. Khan, F. A., Uppal, M., Song, W. C., Kang, M. J., and Mirza, A. M. (2006). FPGA Implementation of a neural network for character recognition. Adv. Neural Netw. 2006, 1357-1365. doi: 10.1007/11760191_197
22. Mokhtar, M., Halliday, D. M., and Tyrrell, A. M. (2008). Hippocampus-inspired spiking neural network on FPGA. Evol. Syst. 5216, 362-371. doi: 10.1007/978-3-540-85857-7_32
23. Moldakarimov, S., Rollenhagen, J. E., Olson, C. R., and Chow, C. C. (2005). Competitive dynamics in cortical responses to visual stimuli. J. Neurophysiol. 94, 3388-3396. doi: 10.1152/jn.00159.2005
24. Muthuramalingam, A., Himavathi, S., and Srinivasan, E. (2008). Neural network implementation using FPGA: issues and application. Int. J. Inform. Technol. 4, 86-92.
25. Pourhaj, P., Teng, D. Y., Wahid, K., and Ko, S. B. (2010). "A novel scalable parallel architecture for biological neural simulations," in Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS) (Paris), 3152-3155. doi: 10.1109/ISCAS.2010.5537951
26. Rice, K. L., Bhuiyan, M. A., Taha, T. M., Vutsinas, C. N., and Smith, M. C. (2009). "FPGA implementation of izhikevich spiking neural networks for character recognition," in International Conference on Reconfigurable Computing and FPGAs, 2009. ReConFig'09 (Quintana Roo), 451-456. doi: 10.1109/ReConFig.2009.77
27. Sahin, S., Becerikli, Y., and Yazici, S. (2006). "Neural network implementation in hardware using FPGAs," in Neural Information Processing, eds I. King, J. Wang, L.-W. Chan, and D. Wang (Berlin; Heidelberg: Springer), 1105-1112.
28. Saïghi, S., Levi, T., Belhadj, B., Malot, O., and Tomas, J. (2010a). "Hardware system for biologically realistic, plastic, and real-time spiking neural network simulations," in The 2010 International Joint Conference on Neural Networks (IJCNN) (Barcelona), 1-7. doi: 10.1109/IJCNN.2010.5596979
29. Saïghi, S., Tomas, J., Bornat, Y., Belhadj, B., Malot, O., and Renaud, S. (2010b). "Real-time multi-board architecture for analog spiking neural networks," in Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS) (Paris), 1939-1942. doi: 10.1109/ISCAS.2010.5538039
30. Traub, R. D., and Miles, R. (1991). Neuronal Networks of the Hippocampus. Cambridge: Cambridge University Press. doi: 10.1017/CBO9780511895401
31. Wang, R. M., Hamilton, T. J., Tapson, J. C., and van Schaik, A. (2014). A mixed-signal implementation of a polychronous spiking neural network with delay adaptation. Front. Neurosci. 8:51. doi: 10.3389/fnins.2014.00051
32. Wanhammar, L. (1999). DSP Integrated Circuits. Academic Press.
33. Yang, S., Wu, Q., and Li, R. (2011). A case for spiking neural network simulation based on configurable multiple-FPGA systems. Cogn. Neurodyn. 5, 301-309. doi: 10.1007/s11571-011-9170-0
34. Zhang, Y., Nunez-Yanez, J., McGeehan, J., Regan, E., and Kelly, S. (2009). "A Biophysically accurate floating point somatic neuroprocessor," in International Conference on Field Programmable Logic and Applications, 2009. FPL 2009 (Prague), 26-31. doi: 10.1109/FPL.2009.5272558
35. Zou, Q., Bornat, Y., Tomas, J., Renaud, S., and Destexhe, A. (2006). Real-time simulations of networks of Hodgkin-Huxley neurons using analog circuits. Neurocomputing 69, 1137-1140. doi: 10.1016/j.neucom.2005.12.061