Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks

Petro Balint, Nikola Kasabov, Rita Kiss

Research output: Contribution to journalArticlepeer-review

76 Citations (Scopus)
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Spiking neural networks (SNNs) receive trains of spiking events as inputs. In order to design efficient SNN systems, real-valued signals must be optimally encoded into spike trains so that the task-relevant information is retained. This paper provides a systematic quantitative and qualitative analysis and guidelines for optimal temporal encoding. It proposes a method- ology of a three-step encoding workflow: method selection by signal
characteristics, parameter optimization by error metrics between original and reconstructed signals, and validation by comparison of the original signal and the encoded spike train. Four encoding methods are analyzed: one stimulus estimation [Ben’s Spiker algorithm (BSA)] and three temporal contrast [threshold-based, step-forward (SW), and moving-window (MW)] encodings. A short theoretical analysis is provided, and the extended quantitative analysis is carried out applying four types of test signals: step-wise signal, smooth (sinusoid) signal with added noise, trended smooth signal, and event-like smooth signal. Various time-domain and frequency spectrum properties are explored, and a comparison is provided. BSA, the only method providing unipolar spikes, was shown to be ineffective for step-wise signals, but it can follow smoothly changing signals if filter coefficients are scaled appropriately. Producing bipolar (positive and negative) spike trains, SW encoding was most effective for all types of signals as it proved to be robust and easy to optimize. Signal-to-noise ratio (SNR) can be recommended as the error metric for parameter optimization. Currently, only a visual check is available for final validation.
Original languageEnglish
Article number8689349
Pages (from-to)358-370
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number2
Early online date12 Apr 2019
Publication statusPublished (in print/issue) - 5 Feb 2020


  • Signal processing
  • spike encoding
  • spiking neural networks (SNNs)
  • stimulus estimation
  • temporal contrast


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