Abstract
In recent years, the implementation of spiking neural models to understand how spiking neural networks interact is focused in many neuroscience papers. The main purpose in this file is finding the solution to behave different neurological diseases. This implementation should be examined and analyzed in two perspectives: model presentation and hardware implementation. In this paper, Morris–Lecar model is selected based on including more biological parameters to match the spiking behaviors of neural system. Due to the nonlinear nature of the differential equations of Morris–Lecar model including hyperbolic functions and multiplicative calculations, linear approximation of the equations is proposed to achieve the low-cost and high-speed realization. The approach of linear fragmentation of Morris–Lecar model leads to an efficient Field-Programmable Gate Arrays (FPGA) implementation with higher frequency and simpler structure. In addition, hyperbolic function is modeled by the suggested \({2}^{\text{X}}\) module and the multiplicative calculations are done based on simple arithmetic operations and logical shift which causes more improvements in the final realization.
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References
Akbarzadeh-Sherbaf K et al (2018) A scalable FPGA architecture for randomly connected networks of hodgkin-huxley neurons. Front Neurosci 12:698
FitzHugh R (1961) Impulses and physiological states in theoretical models of nerve membrane. Biophys J 1(6):445–466
George R et al (2020) Plasticity and adaptation in neuromorphic biohybrid systems. Iscience 23:101589
Gerstner W, Kistler WM (2002) Spiking neuron models: single neurons, populations, plasticity. Cambridge Univ. Press, Cambridge
Ghiasi A, Zahedi A (2022) Field-programmable gate arrays-based Morris–Lecar implementation using multiplierless digital approach and new divider-exponential modules. Comput Electr Eng 99:107771
Grassia F, Lévi T, Saïghi S, Kohno T (2012) Bifurcation analysis in a silicon neuron. Artif Life Robot 17(1):53–58
Haghiri S et al (2018) Multiplierless implementation of noisy Izhikevich neuron with low-cost digital design. IEEE Transact Biomed Circuits Syst 12(6):1422–1430
Hayati M, Nouri M, Haghiri S, Abbott D (2015) Digital multiplierless realization of two coupled biological Morris–Lecar neuron model. IEEE Trans Circuits Syst Regul Pap 62(7):1805–1814
Hishiki T, Torikai H (2011) A novel rotate-and-fire digital spiking neuron and its neuron-like bifurcations and responses. IEEE Trans Neural Networks 22(5):752–767
Hodgkin AL, Huxley AF (1990) A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull Math Biol 52(1–2):25–71
Indiveri G, Chicca E, Douglas R (2006) A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans Neural Networks 17(1):211–221
Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Networks 14(6):1569–1572
Izhikevich EM (2007) Dynamical systems in neuroscience. MIT Press
Kaveh M, Khishe M, Mosavi MR (2019) Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network. Analog Integr Circ Sig Process 100:405–428. https://doi.org/10.1007/s10470-018-1366-3
Khalil HK (2002) Nonlinear systems. Prentice-Hall, Upper Saddle River
Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338
Khoyratee F et al (2019) Optimized real-time biomimetic neural network on FPGA for bio-hybridization. Front Neurosci 13:377
Kohno T, Aihara K (2005) A MOSFET-based model of a class 2 nerve membrane. IEEE Trans Neural Netw 16(3):754–773
Mohammadzadeh A, Taghavifar H (2020) A robust fuzzy control approach for path-following control of autonomous vehicles. Soft Comput 24:3223–3235. https://doi.org/10.1007/s00500-019-04082-4
Morris C, Lecar H (1981) Voltage oscillations in the barnacle giant musclefiber. Biophys J 35(1):193–213
Mosavi MR, Kaveh M, Khishe M, Aghababaee M (2016) Design and implementation a sonar data set classifier by using MLP NN trained by improved biogeography-based optimization. In: proceedings of the second National Conference on marine technology, pp. 1–6
Mosavi MR, Kaveh M, Khishe M, Aghababaie M (2018) Design and implementation a sonar data set classifier using multi-layer perceptron neural network trained by elephant herding optimization. Iranian J Marine Technol 5(1):1–12
Mosbacher Y et al (2020) Toward neuroprosthetic real-time communication from in silico to biological neuronal network via patterned optogenetic stimulation. Sci Rep 10(1):1–16
Omidvar M, Zahedi A, Bakhshi H (2021) EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers. J Ambient Intell Human Comput 12:10395–10403. https://doi.org/10.1007/s12652-020-02837-8
Ostad-Ali-Askari K, Shayan M (2021) Subsurface drain spacing in the unsteady conditions by HYDRUS-3D and artificial neural networks. Arab J Geosci 14:1936. https://doi.org/10.1007/s12517-021-08336-0
Ostad-Ali-Askari et al (2017) Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran. KSCE J Civ Eng 21:134–140. https://doi.org/10.1007/s12205-016-0572-8
Pearson MJ, Pipe AG, Mitchinson B, Gurney K, Melhuish C, Gilhespy I, Nibouche M (2007) Implementing spiking neural networks for real-time signal-processing and control applications: a model-validated FPGA approach. IEEE Trans Neural Netw 18(5):1472–1487
Rahimi Azghadi M, Al-Sarawi SF, Abbott D, Iannella N (2013) A neuromorphic VLSI design for spike timing and rate based synaptic plasticity. Neural Netw 45:70–82
Rahimi Azghadi M et al (2014) Spike-based synaptic plasticity in silicon: design, implementation, application, and challenges. Proc IEEE 102(5):717–737
Schneidman E, Freedman B, Segev I (1998) Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural Comput 10(7):1679–1703
Sharifipour O, Ahmadi A (2012) An analog implementation of biologically plausible neurons using CCII building blocks. Neural Netw 36(1):129–135
Smith GC (2019) Cellular biophysics and modeling: a primer on the computational biology of excitable cells. Cambridge University Press,
Touboul J, Brette R (2008) Dynamics and bifurcations of the adaptive exponential integrate-and-fire model. Biol Cybern 99(4):319–334
Yaghini Bonabi S et al (2014) FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model. Front Neurosci 8:379
Zahedi A, Haghiri S, Hayati M (2019) Multiplierless digital implementation of time-varying FitzHugh–Nagumo model. IEEE Trans Circuits Syst I Regul Pap 66(7):2662–2670
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Authors would like to acknowledge the financial support of Kermanshah University of Technology for this research under grant number S/P/T/1438.
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Ghiasi, A., Zahedi, A. & Haghiri, S. Linear fragmentation Morris–Lecar realization using new exponential module instead of hyperbolic function in FPGA implementation. J Ambient Intell Human Comput 14, 4355–4370 (2023). https://doi.org/10.1007/s12652-023-04546-4
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DOI: https://doi.org/10.1007/s12652-023-04546-4