This paper presents a novel approach for signal reconstruction using Spiking Neural Networks (SNN) based on the principles of Cognitive Informatics and Cognitive Computing. The proposed SNN leverages the Discrete Fourier Transform (DFT) to represent and reconstruct arbitrary time series signals. By employing N spiking neurons, the SNN captures the frequency components of the input signal, with each neuron assigned a unique frequency. The relationship between the magnitude and phase of the spiking neurons and the DFT coefficients is explored, enabling the reconstruction of the original signal. Additionally, the paper discusses the encoding of impulse delays and the phase differences between adjacent frequency components. This research contributes to the field of signal processing and provides insights into the application of SNN for cognitive signal analysis and reconstruction.

翻译：本文提出了一种基于认知信息学与认知计算原理，利用脉冲神经网络进行信号重构的新方法。所提出的脉冲神经网络采用离散傅里叶变换来表征和重构任意时间序列信号。通过使用N个脉冲神经元，该网络能够捕获输入信号的频率分量，其中每个神经元被分配一个特定的频率。本文探讨了脉冲神经元的幅值、相位与离散傅里叶变换系数之间的关系，从而实现了对原始信号的重构。此外，论文还讨论了脉冲延迟的编码方式以及相邻频率分量之间的相位差。本研究对信号处理领域作出了贡献，并为脉冲神经网络在认知信号分析与重构中的应用提供了新的见解。