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

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 withi...

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Vydáno v:Nature communications Ročník 12; číslo 1; s. 4008 - 13
Hlavní autoři: Hochstetter, Joel, Zhu, Ruomin, Loeffler, Alon, Diaz-Alvarez, Adrian, Nakayama, Tomonobu, Kuncic, Zdenka
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 29.06.2021
Nature Publishing Group
Nature Portfolio
Témata:
639/766/530/2795
639/766/530/2801
639/766/530/2803
639/925/357/1016
639/925/929/115
Avalanches
Brain
Cognitive tasks
Data processing
Dynamics
Humanities and Social Sciences
Information processing
Learning
multidisciplinary
Nanotechnology
Nanowires
Neural networks
Optimization
Phase transitions
Recurrent neural networks
Science
Science (multidisciplinary)
Switching
Synapses
Task complexity
ISSN:2041-1723, 2041-1723
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Shrnutí: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. Neuromorphic nanowire networks are found to exhibit neural-like dynamics, including phase transitions and avalanche criticality. Hochstetter and Kuncic et al. show that the dynamical state at the edge-of-chaos is optimal for learning and favours computationally complex information processing tasks.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-021-24260-z