  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.
