Paper
8 November 2024 Phase encoding and time-frequency analysis in polar coordinates for spiking neural network design
Lei Zhang
Author Affiliations +
Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134161A (2024) https://doi.org/10.1117/12.3049556
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China
Abstract
This paper presents a novel phase encoding scheme for spike trains in Spiking Neural Networks (SNNs) inspired by Fourier series and the Discrete Fourier Transform (DFT). The proposed method leverages complex exponential spiking neurons to represent frequency components, allowing for the efficient reconstruction of original time signals. We explore the time shifting property of the Fourier transform to demonstrate how time delays in impulse signals can be encoded as phase shifts in the frequency domain. Detailed mathematical formulations and illustrative examples highlight the relationship between impulse delays and phase patterns in SNNs. The primary objective of this research is to develop a streamlined and computationally efficient SNN architecture, enhancing the training process. Future work will expand this phase encoding method to various sequence patterns, aiming to improve the performance and versatility of SNNs in neuromorphic computing for complex information processing tasks. The results indicate that this approach holds promise for advancing the field by providing a robust framework for precise signal reconstruction and efficient neural network design.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Lei Zhang "Phase encoding and time-frequency analysis in polar coordinates for spiking neural network design", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134161A (8 November 2024); https://doi.org/10.1117/12.3049556
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Neurons

Fourier transforms

Artificial neural networks

Design

Signal processing

Time-frequency analysis

Neural networks

Back to Top