TY  - JOUR
T1  - Exploring The Izhikevich Neuron Model for Robust Signal Encoding and Reconstruction
TT  - Sinyal Kodlama ve Yeniden Yapılandırma İçin Izhikevich Nöron Modelinin İncelenmesi
AU  - Baran, Ahmet Yasin
AU  - Abdalla, Obeid
AU  - Randrıanantenaına, Jean Luck
AU  - Kılıç, Recai
PY  - 2025
DA  - July
Y2  - 2025
JF  - Kayseri Üniversitesi Mühendislik ve Fen Bilimleri Dergisi
JO  - KAYÜ J. Eng.and Sci.
PB  - Kayseri Üniversitesi
WT  - DergiPark
SN  - 3108-415X
SP  - 7
EP  - 11
VL  - 1
IS  - 1
LA  - en
AB  - Traditional signal encoding in neuromorphic systems often relies on simplified neuron models such as the Leaky Integrateand-Fire (LIF) to convert analog inputs into spike trains. However, these models typically demand high firing rates for timevarying signals, resulting in increased energy consumption. In this work, we explore the Izhikevich (IZ) neuron model as a low-power, biologically inspired alternative for signal encoding. We show that the IZ neuron exhibits behavior analogous to a 1-bit Sigma-Delta (ΣΔ) modulator by encoding the input’s amplitude into spike timing. To assess its performance, sinusoidal input signals with added Gaussian white noise (GWN) at various signal-to-noise ratios (SNRs) have been applied to IZ neuron. The spike trains are decoded by analyzing interspike intervals (ISIs) and estimating the instantaneous firing rate to reconstruct the original signal. The robustness of the encoding scheme has been evaluated by measuring the mean squared error (MSE) of the reconstructed signal across SNR levels. Results indicate that the IZ model maintains high reconstruction fidelity under noisy conditions, demonstrating its suitability for robust, event-driven signal encoding in neuromorphic systems.
KW  - neuron model
KW  - Izhıkevich
KW  - Signal encoding
KW  - Sigma-delta modulatiın
KW  - Gaussian white noise
N2  - Nöromorfik sistemlerde geleneksel sinyal kodlama yöntemleri genellikle analog girişleri ateşleme dizilerine dönüştürmek için Sızıntılı Toplayıcı-Ateşleyici (Leaky Integrate-and-Fire – LIF) gibi basitleştirilmiş nöron modellerine dayanır. Ancak bu modeller, zamanla değişen sinyaller için genellikle yüksek ateşleme oranları gerektirir ve bu da enerji tüketimini artırır. Bu çalışmada, sinyal kodlaması için biyolojik anlamlılığı yüksek ve düşük güç tüketimine sahip Izhikevich (IZ) nöron modeli incelenmiştir. IZ nöronunun, giriş genliğini ateşleme zamanlamasına kodlayarak 1-bit Sigma-Delta (ΣΔ) modülatörüne benzer davranış sergilediği gösterilmiştir. Modelin performansını değerlendirmek için farklı sinyal-gürültü oranlarında (SNR) Gauss beyaz gürültüsü (GWN) eklenmiş sinüzoidal giriş sinyalleri IZ nöronuna uygulanmıştır. Oluşan ateşleme dizileri, ateşlemeler arası zaman aralıkları (interspike intervals – ISIs) analiz edilerek ve anlık ateşleme oranı tahmin edilerek çözülmüş; böylece orijinal sinyal yeniden yapılandırılmıştır. Kodlama yapısının dayanıklılığı, farklı SNR seviyelerinde yeniden yapılandırılan sinyalin ortalama kare hata (MSE) değeri ölçülerek değerlendirilmiştir. Sonuçlar, IZ modelinin gürültülü koşullarda dahi yüksek yeniden yapılandırma doğruluğunu koruduğunu göstermekte ve bu modeli nöromorfik sistemlerde dayanıklı, olaytemelli sinyal kodlaması için uygun bir seçenek haline getirmektedir.
CR  - Kang, M., Lee, Y., Park, M.: Energy Efficiency of Machine Learning in Embedded Systems Using Neuromorphic Hardware. Electronics (Basel). 9, 1069 (2020). https://doi.org/10.3390/electronics9071069
CR  - Yang, Y., Bartolozzi, C., Zhang, H.H., Nawrocki, R.A.: Neuromorphic electronics for robotic perception, navigation and control: A survey. Eng Appl Artif Intell. 126, 106838 (2023). https://doi.org/10.1016/j.engappai.2023.106838
CR  - Rajendran, B., Sebastian, A., Schmuker, M., Srinivasa, N., Eleftheriou, E.: Low-Power Neuromorphic Hardware for Signal Processing Applications. (2019). https://doi.org/10.1109/MSP.2019.2933719
CR  - Tavanaei, A., Ghodrati, M., Kheradpisheh, S.R., Masquelier, T., Maida, A.: Deep learning in spiking neural networks. Neural Networks. 111, 47–63 (2019). https://doi.org/10.1016/j.neunet.2018.12.002
CR  - Auge, D., Hille, J., Mueller, E., Knoll, A.: A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks. Neural Process Lett. 53, 4693–4710 (2021). https://doi.org/10.1007/s11063-021-10562-2
CR  - Brette, R.: Dynamics of one-dimensional spiking neuron models. J Math Biol. 48, 38–56 (2004). https://doi.org/10.1007/s00285-003-0223-9
CR  - Nair, M. V, Indiveri, G.: An Ultra-Low Power Sigma-Delta Neuron Circuit. In: 2019 IEEE International Symposium on Circuits and Systems (ISCAS). pp. 1–5. IEEE (2019). https://doi.org/10.1109/ISCAS.2019.8702500
CR  - Yoon, Y.C.: LIF and Simplified SRM Neurons Encode Signals Into Spikes via a Form of Asynchronous Pulse Sigma–Delta Modulation. IEEE Trans Neural Netw Learn Syst. 28, 1192–1205 (2017). https://doi.org/10.1109/TNNLS.2016.2526029
CR  - Hovin, M., Wisland, D., Berg, Y., Marienborg, J.T., Lande, T.S.: Delta-sigma modulation in single neurons. In: 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353). pp. V-617-V–620. IEEE. https://doi.org/10.1109/ISCAS.2002.1010779
CR  - Thao, N.T., Rzepka, D., Miśkowicz, M.: Bandlimited Signal Reconstruction From Leaky Integrate-and-Fire Encoding Using POCS. IEEE Transactions on Signal Processing. 71, 1464–1479 (2023). https://doi.org/10.1109/TSP.2023.3256269
CR  - Dazhi Wei, Harris, J.G.: Signal reconstruction from spiking neuron models. In: 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512). pp. V-353-V–356. IEEE. https://doi.org/10.1109/ISCAS.2004.1329535
CR  - Schrauwen, B., Van Campenhout, I.: BSA, a fast and accurate spike train encoding scheme. In: Proceedings of the International Joint Conference on Neural Networks, 2003. pp. 2825–2830. IEEE. https://doi.org/10.1109/IJCNN.2003.1224019
CR  - Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans Neural Netw. 14, 1569–1572 (2003). https://doi.org/10.1109/TNN.2003.820440
CR  - Izhikevich, E.M.: Which Model to Use for Cortical Spiking Neurons? IEEE Trans Neural Netw. 15, 1063–1070 (2004). https://doi.org/10.1109/TNN.2004.832719
CR  - Janssen, E., van Roermund, A.: Basics of Sigma-Delta Modulation. Presented at the (2011). https://doi.org/10.1007/978-94-007-1387-1_2
CR  - Ko, D., Wilson, C.J., Lobb, C.J., Paladini, C.A.: Detection of bursts and pauses in spike trains. J Neurosci Methods. 211, 145–158 (2012). https://doi.org/10.1016/j.jneumeth.2012.08.013
CR  - Lánský, P., Rodriguez, R., Sacerdote, L.: Mean Instantaneous Firing Frequency Is Always Higher Than the Firing Rate. Neural Comput. 16, 477–489 (2004). https://doi.org/10.1162/089976604772744875
UR  - https://dergipark.org.tr/tr/pub/kayumuhvefenderg/issue//1728978
L1  - https://dergipark.org.tr/tr/download/article-file/4998295
ER  -