Abstract
An algorithm called Symmetric Integro-Differential Conversion (SIDC) serving to encode continuous real-valued signal to spike form is described. One dynamic numeric signal is converted to 4 spike trains. 2 spiking signal lines serve as a low-pass filter, while 2 other play the role of high-pass filter. The algorithm is computationally efficient and has only one parameter – the desired mean spike frequency in the output spike sequences. This approach allows accurate encoding signals with high variability of spectral properties. A reverse conversion algorithm is proposed which is used to assure that the resulting spike signal preserves information about the original signal to a sufficient extent. Artificial signals being a sum of a sinusoid and a random walk process are utilized to show that the target spike frequency parameter does not require fine tuning - good conversion quality is demonstrated if its value is approximately two orders of magnitude less than the input signal measurement frequency.
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Gerstner, W., Kistler, W.: Spiking Neuron Models. Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)
Brette, R.: Philosophy of the spike: rate-based vs. spike-based theories of the brain. Front. Syst. Neurosci. 9, 151 (2015)
Kiselev, M.: Rate coding vs. temporal coding – is optimum between? In: Proceedings of IJCNN-2016, Vancouver, pp. 1355–1359 (2016)
Izhikevich, E.: Polychronization: computation with spikes. Neural Comput. 18, 245–282 (2006)
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Kiselev, M. (2019). A General Purpose Algorithm for Coding/Decoding Continuous Signal to Spike Form. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research II. NEUROINFORMATICS 2018. Studies in Computational Intelligence, vol 799. Springer, Cham. https://doi.org/10.1007/978-3-030-01328-8_20
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DOI: https://doi.org/10.1007/978-3-030-01328-8_20
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