FPGA Implementation of Hodgkin-Huxley Neuron Model

Safa Yaghini Bonabi, Hassan Asgharian, Reyhaneh Bakhtiari, Saeed Safari, Majid Nili Ahmadabadi

Abstract

In this paper an implementation of Hodgkin-Huxley single neuron is provided. Unlike almost all of the existing implementations, the arithmetic logics are implemented with computation techniques (i.e. CORDIC) and look-up-tables (LUTs) are used only in few modules. This makes our design more robust and flexible to simulate the functionality of a large network of neurons. Most of the previous works are based on the software implementations which overshadow the parallel nature of the neural system or using LUTs for hardware implementation which needs more space and also limited flexibility. In this paper, an FPGA is selected as our hardware implementation platform to provide an appropriate reconfigurable platform for simulating the functionality of a network of neurons. We validated our design based on our high level implementation of Hodgkin-Huxley neuron in MATLAB and report our implementation results based on Xilinx SPARTAN 3 FPGA in Xilinx ISE Design Suite.

References

  1. Bakhtiari, R., Sepahvand, N. M., Ahmadabadi, M. N., Araabi, B. N., Esteky, H., 2012. Computational model of excitatory/inhibitory ratio imbalance role in attention deficit disorders. Computational Neuroscience.
  2. Ercegovac, M. D., Lang, T., 2003. Digital Arithmetic, 1st ed. Morgan Kaufmann.
  3. Gatet, L., Tap-B├ęteille, H., Bony, F., 2009. Comparison between analog and digital neural network implementations for range-finding applications. Neural Networks, IEEE Transactions on 20, 460-470.
  4. Graas, E. L., Brown, E. A., Lee, R. H., 2004. An FPGAbased approach to high-speed simulation of conductance-based neuron models. Neuroinformatics 2, 417-435.
  5. Hodgkin, A. L., Huxley, A. F., 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. (Lond.) 117, 500-544.
  6. Izhikevich, E. M., 2007. Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT Press.
  7. Kandel, E. R., Schwartz, J. H., Jessell, T. M., others, 2000. Principles of neural science. McGraw-Hill New York.
  8. Li, G., Talebi, V., Yoonessi, A., Baker, C. L., Jr, 2010. A FPGA real-time model of single and multiple visual cortex neurons. J. Neurosci. Methods 193, 62-66.
  9. Mokhtar, M., Halliday, D. M., Tyrrell, A. M., 2008. Hippocampus-Inspired Spiking Neural Network on FPGA, in: Proceedings of the 8th International Conference on Evolvable Systems: From Biology to Hardware, ICES 7808. Springer-Verlag, Berlin, Heidelberg, pp. 362-371.
  10. Muthuramalingam, A., Himavathi, S., Srinivasan, E., 2008. Neural network implementation using FPGA: Issues and application. International journal of information technology 4, 86-92.
  11. Pourhaj, P., Teng, D.H.., 2010. FPGA based pipelined architecture for action potential simulation in biological neural systems, in: Electrical and Computer Engineering (CCECE), 2010 23rd Canadian Conference On. pp. 1-4.
  12. Rice, K. L., Bhuiyan, M. A., Taha, T. M., Vutsinas, C. N., Smith, M.C., 2009. FPGA implementation of Izhikevich spiking neural networks for character recognition, in: Reconfigurable Computing and FPGAs, 2009. ReConFig'09. International Conference On. pp. 451-456.
  13. Wanhammar, L., 1999. DSP integrated circuits. Academic Press.
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Paper Citation


in Harvard Style

Yaghini Bonabi S., Asgharian H., Bakhtiari R., Safari S. and Nili Ahmadabadi M. (2012). FPGA Implementation of Hodgkin-Huxley Neuron Model . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 522-528. DOI: 10.5220/0004152605220528


in Bibtex Style

@conference{ncta12,
author={Safa Yaghini Bonabi and Hassan Asgharian and Reyhaneh Bakhtiari and Saeed Safari and Majid Nili Ahmadabadi},
title={FPGA Implementation of Hodgkin-Huxley Neuron Model},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={522-528},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004152605220528},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - FPGA Implementation of Hodgkin-Huxley Neuron Model
SN - 978-989-8565-33-4
AU - Yaghini Bonabi S.
AU - Asgharian H.
AU - Bakhtiari R.
AU - Safari S.
AU - Nili Ahmadabadi M.
PY - 2012
SP - 522
EP - 528
DO - 10.5220/0004152605220528