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Avalanches and edge-of-chaos learning in neuromorphic nanowire networks

Author

Listed:
  • Joel Hochstetter

    (School of Physics, University of Sydney)

  • Ruomin Zhu

    (School of Physics, University of Sydney)

  • Alon Loeffler

    (School of Physics, University of Sydney)

  • Adrian Diaz-Alvarez

    (International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS))

  • Tomonobu Nakayama

    (School of Physics, University of Sydney
    International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS)
    Graduate School of Pure and Applied Sciences, University of Tsukuba)

  • Zdenka Kuncic

    (School of Physics, University of Sydney
    International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS)
    The University of Sydney Nano Institute)

Abstract

The brain’s efficient information processing is enabled by the interplay between its neuro-synaptic elements and complex network structure. This work reports on the neuromorphic dynamics of nanowire networks (NWNs), a unique brain-inspired system with synapse-like memristive junctions embedded within a recurrent neural network-like structure. Simulation and experiment elucidate how collective memristive switching gives rise to long-range transport pathways, drastically altering the network’s global state via a discontinuous phase transition. The spatio-temporal properties of switching dynamics are found to be consistent with avalanches displaying power-law size and life-time distributions, with exponents obeying the crackling noise relationship, thus satisfying criteria for criticality, as observed in cortical neuronal cultures. Furthermore, NWNs adaptively respond to time varying stimuli, exhibiting diverse dynamics tunable from order to chaos. Dynamical states at the edge-of-chaos are found to optimise information processing for increasingly complex learning tasks. Overall, these results reveal a rich repertoire of emergent, collective neural-like dynamics in NWNs, thus demonstrating the potential for a neuromorphic advantage in information processing.

Suggested Citation

  • Joel Hochstetter & Ruomin Zhu & Alon Loeffler & Adrian Diaz-Alvarez & Tomonobu Nakayama & Zdenka Kuncic, 2021. "Avalanches and edge-of-chaos learning in neuromorphic nanowire networks," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24260-z
    DOI: 10.1038/s41467-021-24260-z
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    Cited by:

    1. Gianluca Milano & Alessandro Cultrera & Luca Boarino & Luca Callegaro & Carlo Ricciardi, 2023. "Tomography of memory engrams in self-organizing nanowire connectomes," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Woo, Junhyuk & Kim, Soon Ho & Kim, Hyeongmo & Han, Kyungreem, 2024. "Characterization of the neuronal and network dynamics of liquid state machines," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    3. Angela Slavova & Ventsislav Ignatov, 2022. "Edge of Chaos in Memristor Cellular Nonlinear Networks," Mathematics, MDPI, vol. 10(8), pages 1-11, April.
    4. Ruomin Zhu & Sam Lilak & Alon Loeffler & Joseph Lizier & Adam Stieg & James Gimzewski & Zdenka Kuncic, 2023. "Online dynamical learning and sequence memory with neuromorphic nanowire networks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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