Spiking Neural Network Phase Encoding for Cognitive Computing

TL;DR

This paper introduces a novel method using Spiking Neural Networks and DFT for reconstructing signals, advancing cognitive computing applications in signal processing.

Contribution

It presents a new SNN-based approach that encodes frequency and phase information for signal reconstruction, integrating cognitive computing principles.

Findings

Effective reconstruction of signals using SNN and DFT

Insight into phase and delay encoding in SNN

Potential applications in cognitive signal analysis

Abstract

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.