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Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data
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DOI：10.1109/TNNLS.2019.2947380.png)
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摘要
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This article proposes a new spike encoding and decoding algorithm for analog data. The algorithm uses the pulsewidth modulation principles to achieve a high reconstruction accuracy of the signal, along with a high level of data compression. Two benchmark data sets are used to illustrate the method: stock index time series and human voice data. Applications of the method for spiking neural network (SNN) modeling and neuromorphic implementations are discussed. The proposed method would allow the development of new applications of SNNs as regression techniques for predictive time-series modeling.
Keyword:
Encoding
Neurons
Pulse width modulation
Decoding
Signal processing algorithms
Heuristic algorithms
Data models
Analog data
data compression
spike encoding
spike series decoding
spiking neural networks (SNNs)
streaming data
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