Modular design and implementation of biological neuron models with FPGA hardware

Year 2025, Volume: 1 Issue: 1, 12 - 21, 30.07.2025

Sılaynur Doğan , Onur Güneş , İsmail Öztürk

Abstract

In this study, a standard and modular design approach is proposed for digital hardware implementations of biological neuron models. According to this design approach, all neuron models are implemented in a single standard format regardless of mathematical equations, parameters and operating modes they can exhibit. Afterwards, instead of using neuron models implemented in the same format separately on the hardware, these neuron models can be combined into a single block structure and used simultaneously. This modular approach provides rapid prototyping and design flexibility in neuromorphic applications. As an example design, Izhikevich, Fitzhugh-Nagumo (FHN), Hindmarsh–Rose (HR) and Leaky Integrate-and-Fire (LIF) neuron models were selected and combined in the proposed structure. The hardware implementation of the proposed structure was performed on FPGA (Field Programmable Gate Array). The outputs were observed with an oscilloscope and the obtained results are in agreemeent with the simulations.

Keywords

Biological neuron model , FPGA , Neuromorphic hardware

FPGA donanımı ile biyolojik nöron modellerinin modüler olarak tasarımı ve gerçekleştirimi

Abstract

Bu çalışmada biyolojik nöron modellerinin dijital donanım gerçekleştirimleri için standart ve modüler bir tasarım yaklaşımı önerilmektedir. Bu tasarım yaklaşımına göre matematiksel denklem, parametre ve sergileyebildiği çalışma modlarına bakmaksızın bütün nöron modellerinin tek bir standart formatta gerçekleştirimi yapılmaktadır. Sonrasında ise aynı formatta gerçekleştirilen nöron modellerinin, donanım üzerinde ayrı ayrı kullanılması yerine bu nöron modelleri tek bir blok yapı içerisinde birleştirilerek aynı anda kullanılabilmektedir. Bu modüler yaklaşım nöromorfik uygulamalarda hızlı prototiplendirme ve tasarım esnekliği sağlamaktadır. Örnek bir tasarım olarak Izhikevich, Fitzhugh-Nagumo (FHN), Hindmarsh–Rose (HR) ve Leaky Integrate-and-Fire (LIF) nöron modelleri seçilmiş ve önerilen yapıda birleştirilmiştir. Önerilen yapının donanım gerçekleştirimi ise FPGA (Field Programmable Gate Array) üzerinde yapılmıştır. Çıkışlar osiloskop ile gözlemlenmiş olup elde edilen sonuçlar simülasyonlar ile uyumludur.

Keywords

Biyolojik nöron modeli , FPGA , Nöromorfik donanım

Thanks

Bu çalışma 1919B012321491 numaralı Tübitak 2209-A projesi tarafından desteklenmiştir.

References

• Yang, J. Q., Wang, R., Ren, Y., Mao, J. Y., Wang, Z. P., Zhou, Y., & Han, S. T. (2020). Neuromorphic engineering: from biological to spike‐based hardware nervous systems. Advanced Materials, 32(52), 2003610. https://doi.org/10.1002/adma.202003610
• Liu, W., Xiao, S., Li, B., & Yu, Z. (2024). SC-IZ: A low-cost biologically plausible Izhikevich neuron for large-scale neuromorphic systems using stochastic computing. Electronics, 13(5), 909. https://doi.org/10.1109/TBCAS.2020.2995869
• Alkabaa, A. S., Taylan, O., Yilmaz, M. T., Nazemi, E., & Kalmoun, E. M. (2022). An investigation on spiking neural networks based on the izhikevich neuronal model: Spiking processing and hardware approach. Mathematics, 10(4), 612. https://doi.org/10.3390/math10040612
• Shrestha, A., Fang, H., Mei, Z., Rider, D. P., Wu, Q., & Qiu, Q. (2022). A survey on neuromorphic computing: Models and hardware. IEEE Circuits and Systems Magazine, 22(2), 6-35. https://doi.org/10.1109/MCAS.2022.3166331
• Yamazaki, K., Vo-Ho, V. K., Bulsara, D., & Le, N. (2022). Spiking neural networks and their applications: A review. Brain sciences, 12(7), 863. https://doi.org/10.3390/brainsci12070863
• Ghosh-Dastidar, S., & Adeli, H. (2009). Spiking neural networks. International journal of neural systems, 19(04), 295-308. https://doi.org/10.1142/S0129065709002002
• Li, Y., Yin, R., Kim, Y., & Panda, P. (2023). Efficient human activity recognition with spatio-temporal spiking neural networks. Frontiers in Neuroscience, 17, 1233037. https://doi.org/10.3389/fnins.2023.1233037
• Hodgkin, A., & Huxley, A. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology. 117, 500–544. https://doi.org/10.1113/jphysiol.1952.sp004764
• FitzHugh, R. (1961). Impulses and physiological states in theoretical models of nerve membrane. Biophysical journal, 1(6), 445-466. https://doi.org/10.1016/s0006-3495(61)86902-6
• Nagumo, J., Arimoto, S., & Yoshizawa, S. (1962). An active pulse transmission line simulating nerve axon. Proceedings of the IRE, 50(10), 2061-2070. https://doi.org/10.1109/JRPROC.1962.288235
• Hindmarsh, J. L., & Rose, R. M. (1984). A model of neuronal bursting using three coupled first order differential equations. Proceedings of the Royal society of London. Series B. Biological sciences, 221(1222), 87-102. https://doi.org/10.1098/rspb.1984.0024
• Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical journal, 35(1), 193-213. https://doi.org/10.1016/S0006-3495(81)84782-0
• Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on neural networks, 14(6), 1569-1572. https://doi.org/10.1109/TNN.2003.820440
• Burkitt, A. N. (2006). A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biological cybernetics, 95(1), 1-19. https://doi.org/10.1007/s00422-006-0068-6
• Ermentrout, G. B., & Kopell, N. (1986). Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM journal on applied mathematics, 46(2), 233-253. https://doi.org/10.1137/0146017
• Fourcaud-Trocmé, N., Hansel, D., Van Vreeswijk, C., & Brunel, N. (2003). How spike generation mechanisms determine the neuronal response to fluctuating inputs. Journal of neuroscience, 23(37), 11628-11640. https://doi.org/10.1523/JNEUROSCI.23-37-11628.2003
• Bashir, F., Zahoor, F., Alzahrani, A., & Abbas, H. (2025). Energy-efficient neuromorphic system using novel tunnel FET based LIF neuron design for adaptable threshold logic and image analysis applications. Scientific Reports, 15(1), 12638. https://doi.org/10.1038/s41598-025-93727-6
• Fang, X., Liu, D., Duan, S., & Wang, L. (2022). Memristive lif spiking neuron model and its application in morse code. Frontiers in Neuroscience, 16, 853010. https://doi.org/10.3389/fnins.2022.853010
• Aamir, S. A., Stradmann, Y., Müller, P., Pehle, C., Hartel, A., Grübl, A., ... & Meier, K. (2018). An accelerated LIF neuronal network array for a large-scale mixed-signal neuromorphic architecture. IEEE Transactions on Circuits and Systems I: Regular Papers, 65(12), 4299-4312. https://doi.org/10.1109/TCSI.2018.2840718
• Dahasert, N., Öztürk, İ., & Kiliç, R. (2012). Experimental realizations of the HR neuron model with programmable hardware and synchronization applications. Nonlinear Dynamics, 70(4), 2343-2358. https://doi.org/10.1007/s11071-012-0618-5
• Korkmaz, N., Öztürk, İ., & Kilic, R. (2016). Multiple perspectives on the hardware implementations of biological neuron models and programmable design aspects. Turkish Journal of Electrical Engineering and Computer Sciences, 24(3), 1729-1746. https://doi.org/10.3906/elk-1309-5
• Korkmaz, N., Öztürk, İ., Kalinli, A., & Kiliç, R. (2018). A comparative study on determining nonlinear function parameters of the Izhikevich neuron model. Journal of Circuits, Systems and Computers, 27(10), 1850164. https://doi.org/10.1142/S0218126618501645
• Baran, A. Y., Korkmaz, N., Öztürk, I., & Kılıç, R. (2022). On addressing the similarities between STDP concept and synaptic/memristive coupled neurons by realizing of the memristive synapse based HR neurons. Engineering Science and Technology, an International Journal, 32, 101062. https://doi.org/10.1016/j.jestch.2021.09.008
• Guo, W., Yantır, H. E., Fouda, M. E., Eltawil, A. M., & Salama, K. N. (2021). Toward the optimal design and FPGA implementation of spiking neural networks. IEEE Transactions on Neural Networks and Learning Systems, 33(8), 3988-4002. https://doi.org/10.1109/TNNLS.2021.3055421
• Dahasert, N. (2012). Biyolojik nöron modellerinin elektronik donanımlarının incelenmesi [Yüksek lisans tezi, Erciyes Üniversitesi]. https://acikbilim.yok.gov.tr/bitstream/handle/20.500.12812/498682/yokAcikBilim_442064.pdf?sequence=-1
• İşler, Y. S. (2023). Özel Başlangıç Koşulları Altında Lineer LIF Nöron Modelinin Analizi ve Çözüm Metodu. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(3), 1785-1795. https://doi.org/10.47495/okufbed.1184076