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Time-Frequency Analysis for Feature Extraction Using Spiking Neural Network
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  • Moshe Bensimon ,
  • Yakir Hadad ,
  • Yehuda Ben-Shimol ,
  • Shlomo Greenberg
Moshe Bensimon
School of Electrical and Computer Engineering

Corresponding Author:[email protected]

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Yakir Hadad
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Yehuda Ben-Shimol
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Shlomo Greenberg
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Time-frequency analysis plays a crucial role in various fields, including signal processing and feature extraction. In this article, we propose an alternative and innovative method for time-frequency analysis using a biologically inspired spiking neural network (SNN), encompassing both specific spike-continuous-time-neuron (SCTN) based neural architecture and an adaptive learning rule.
We aim to efficiently detect frequencies embedded in a given signal for the purpose of feature extraction. To achieve this, we suggest using an SN-based network functioning as a resonator for the detection of specific frequencies. We developed a modified supervised Spike-Timing-Dependent Plasticity (STDP) learning rule to effectively adjust the network parameters.
Unlike traditional methods for time-frequency analysis, our approach obviates the need for segmenting the signal into several frames, resulting in a streamlined and more effective frequency analysis process.
Simulation results demonstrate the efficiency of the proposed method, showcasing its ability to detect frequencies and  generate a Spikegram akin to the Fast Fourier Transform (FFT) based spectrogram. The proposed approach is applied to analyzing EEG signals demonstrating an accurate correlation to the equivalent FFT transform.