Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 31, Issue 2, Pages 358-370Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2906158
Keywords
Signal processing; spike encoding; spiking neural networks (SNNs); stimulus estimation; temporal contrast
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Funding
- Ministry of Human Capacities through the Higher Education Excellence Program in the frame of biotechnology research area of Budapest University of Technology and Economics (BME FIKP-BIO)
- Erasmus Mundus Action 2-PANTHER Project
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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 methodology 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.
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