Exploring The Izhikevich Neuron Model for Robust Signal Encoding and Reconstruction

Abdalla O., Randrıanantenaına J. L., Baran A. Y., Kılıç R.

Kayseri Üniversitesi Mühendislik ve Fen Bilimleri Dergisi, cilt.1, sa.1, ss.7-11, 2025 (Hakemli Dergi)

Özet

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.

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.