Most biological neurons exhibit stochastic and spiking action potentials. However, the benefits of stochastic spikes versus continuous signals other than noise tolerance and energy efficiency remain largely unknown. In this study, we provide an insight into the potential roles of stochastic spikes, which may be beneficial for producing on-site adaptability in biological sensorimotor agents. We developed a platform that enables parametric modulation of the stochastic and discontinuous output of a stochastically spiking neural network (sSNN) to the rate-coded smooth output. This platform was applied to a complex musculoskeletal-neural system of a bipedal walker, and we demonstrated how stochastic spikes may help improve on-site adaptability of a bipedal walker to slippery surfaces or perturbation of random external forces. We further applied our sSNN platform to more general and simple sensorimotor agents and demonstrated four basic functions provided by an sSNN: 1) synchronization to a natural frequency, 2) amplification of the resonant motion in a natural frequency, 3) basin enlargement of the behavioral goal state, and 4) rapid complexity reduction and regular motion pattern formation. We propose that the benefits of sSNNs are not limited to musculoskeletal dynamics. Indeed, a wide range of the stability and adaptability of biological systems may arise from stochastic spiking dynamics.
Keywords: bipedal walking; instantaneous self-organization; musculoskeletal system; spike-induced ordering; spiking neural network.
Copyright © 2020 the Author(s). Published by PNAS.
Conflict of interest statement
The authors declare no competing interest.
Statistical-mechanical measure of stochastic spiking coherence in a population of inhibitory subthreshold neurons.J Comput Neurosci. 2011 Nov;31(3):667-77. doi: 10.1007/s10827-011-0330-3. Epub 2011 May 3. J Comput Neurosci. 2011. PMID: 21538140
Efficient Multispike Learning for Spiking Neural Networks Using Probability-Modulated Timing Method.IEEE Trans Neural Netw Learn Syst. 2019 Jul;30(7):1984-1997. doi: 10.1109/TNNLS.2018.2875471. Epub 2018 Nov 6. IEEE Trans Neural Netw Learn Syst. 2019. PMID: 30418889
Compact Hardware Synthesis of Stochastic Spiking Neural Networks.Int J Neural Syst. 2019 Oct;29(8):1950004. doi: 10.1142/S0129065719500047. Epub 2019 Feb 8. Int J Neural Syst. 2019. PMID: 30880526
Enhancement of Spike-Timing-Dependent Plasticity in Spiking Neural Systems with Noise.Int J Neural Syst. 2016 Aug;26(5):1550040. doi: 10.1142/S0129065715500409. Epub 2015 Oct 9. Int J Neural Syst. 2016. PMID: 26678248
Overview of facts and issues about neural coding by spikes.J Physiol Paris. 2010 Jan-Mar;104(1-2):5-18. doi: 10.1016/j.jphysparis.2009.11.002. Epub 2009 Nov 29. J Physiol Paris. 2010. PMID: 19925865 Review.