Abstract
Brownian circuits use the fluctuations of signals, implemented by tokens, to drive computation. They have been shown to significantly reduce the required complexity of circuits or platforms implementing these circuits, such as cellular automata. The original model of Brownian circuits eyed computation models in which operations on individual tokens are at the core. This paper discusses models in which collections of tokens are used as signals in Brownian circuits, in particular neural networks. We show how previously proposed Brownian circuit primitives can be employed to implement neural functionality, like thresholding, synchronization, and learning.
This work is supported by JST CREST Grant No. JPMJCR20C1, as well as by JSPS KAKENHI Grants No. 20H01827 and No. 20H05666, all from Japan.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Andreev, A.V., Maksimenko, V.A., Pisarchik, A.N., Hramov, A.E.: Synchronization of interacted spiking neuronal networks with inhibitory coupling. Chaos, Solitons Fractals 146, 110812 (2021)
Dan, Y., Poo, M.: Spike timing-dependent plasticity of neural circuits. Neuron 44(1), 23–30 (2004)
Ermentrout, G.B., Kopell, N.: Fine structure of neural spiking and synchronization in the presence of conduction delays. Proc. Natl. Acad. Sci. 95(3), 1259–1264 (1998)
Faisal, A.A., Selen, L.P.J., Wolpert, D.M.: Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008)
Fei, L.J., Lee, J., Huang, X., Peper, F.: Effect of random fluctuations on minimizing the complexity of universal asynchronous cellular automata. Phys. D: Nonlinear Phenom. 428, 133052 (2021)
Izhikevich, E.M.: Polychronization: computation with Spikes. Neural Comput. 18(2), 245–282 (2006)
Jibiki, Y., Goto, M., Tamura, E., Cho, J., Miki, S., Ishikawa, R., Nomura, H., Srivastava, T., Lim, W., Auffret, S., Baraduc, C., Bea, H., Suzuki, Y.: Skyrmion brownian circuit implemented in continuous ferromagnetic thin film. Appl. Phys. Lett. 117(8), 082402 (2020)
Lee, J., Peper, F.: On brownian cellular automata. In: Proceeding of Automata 2008, pp. 278–291. Luniver Press, UK (2008)
Lee, J., Peper, F., Cotofana, S., Naruse, M., Ohtsu, M., Kawazoe, T., Takahashi, Y., Shimokawa, T., Kish, L., Kubota, T.: Brownian circuits: designs. Int. J. Unconv. Comput. 12(5–6), 341–362 (2016)
Peper, F., Lee, J., Carmona, J., Cortadella, J., Morita, K.: Brownian circuits: fundamentals. ACM J. Emer. Technol. Comput. Syst. 9(1) (2013)
Utsumi, Y., Ito, Y., Golubev, D., Peper, F.: Computation time and thermodynamic uncertainty relation of brownian circuits (2022). arXiv:2205.10735
Zhang, C., Zhang, D., Stepanyants, A.: Noise in neurons and synapses enables reliable associative memory storage in local cortical circuits. eNeuro 8(1) (2021)
Acknowledgements
We thank Prof. Yoshishige Suzuki from Osaka University for the valuable discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Inada, A., Eto, M., Isokawa, T., Utsumi, Y., Nakade, S., Peper, F. (2023). Brownian Circuits: From Computation to Neural Networks. In: Das, S., Martinez, G.J. (eds) Proceedings of Second Asian Symposium on Cellular Automata Technology. ASCAT 2023. Advances in Intelligent Systems and Computing, vol 1443. Springer, Singapore. https://doi.org/10.1007/978-981-99-0688-8_3
Download citation
DOI: https://doi.org/10.1007/978-981-99-0688-8_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0687-1
Online ISBN: 978-981-99-0688-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)